Transfer_Learning
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Pediatric Pneumonia Detection from Chest X-Rays:A Comparative Study of Transfer Learning and Custom CNNs
2025-12-26 18:48:39Agniv Roy ChoudhuryAbstract
Pneumonia is a leading cause of mortality in children under five, with over 700,000 deaths annually. Accurate diagnosis from chest X-rays is limited by radiologist availability and variability. Objective: This study compares custom CNNs trained from scratch with transfer learning (ResNet50, DenseNet121, EfficientNet-B0) for pediatric pneumonia detection, evaluating frozen-backbone and fine-tuning regimes. Methods: A dataset of 5,216 pediatric chest X-rays was split 80/10/10 for training, validation, and testing. Seven models were trained and assessed using accuracy, F1-score, and AUC. Grad-CAM visualizations provided explainability. Results: Fine-tuned ResNet50 achieved the best performance: 99.43\% accuracy, 99.61\% F1-score, and 99.93\% AUC, with only 3 misclassifications. Fine-tuning outperformed frozen-backbone models by 5.5 percentage points on average. Grad-CAM confirmed clinically relevant lung regions guided predictions. Conclusions: Transfer learning with fine-tuning substantially outperforms CNNs trained from scratch for pediatric pneumonia detection, showing near-perfect accuracy. This system has strong potential as a screening tool in resource-limited settings. Future work should validate these findings on multi-center and adult datasets. Keywords: Pneumonia detection, deep learning, transfer learning, CNN, chest X-ray, pediatric diagnosis, ResNet, DenseNet, EfficientNet, Grad-CAM.
Abstract (translated)
肺炎是五岁以下儿童死亡的主要原因之一，每年导致超过70万例死亡。放射科医生的可用性和诊断的一致性限制了胸部X光片上准确诊断肺炎的能力。本研究旨在比较从零开始训练的自定义卷积神经网络（CNN）与迁移学习方法（包括ResNet50、DenseNet121和EfficientNet-B0）在儿童肺炎检测中的表现，同时评估固定骨干模型和微调策略的效果。 **方法：** 使用包含5,216张儿科胸部X光片的数据集进行研究。数据集按照80%用于训练，10%用于验证，剩余的10%用于测试的比例进行了分割。共训练并评估了七种不同模型（包括三种迁移学习模型及其固定和微调版本），使用准确率、F1分数以及AUC指标进行性能评价，并通过Grad-CAM可视化技术提供了模型预测过程中的解释性。 **结果：** 在所有模型中，经过微调的ResNet50表现最佳，其精确率为99.43%，F1评分为99.61%，AUC值为99.93%，仅出现三次误分类情况。相较于固定骨干模型，使用微调策略平均提高了5.5个百分点的表现。Grad-CAM可视化技术确认了临床相关的肺区域确实指导了预测过程。 **结论：** 与从零开始训练的CNN相比，采用迁移学习并结合微调策略的方法在儿童肺炎检测中表现出显著更优的效果，并达到了近乎完美的精确度。这表明该系统有可能成为资源受限环境中的一种有效的筛查工具。未来的工作应该使用多中心及成人数据集对这些发现进行验证。 **关键词：** 肺炎检测、深度学习、迁移学习、CNN、胸部X光片、儿科诊断、ResNet、DenseNet、EfficientNet、Grad-CAM
URL
https://arxiv.org/abs/2601.00837
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Bounded Hyperbolic Tangent: A Stable and Efficient Alternative to Pre-Layer Normalization in Large Language Models
2025-12-26 06:22:13Hoyoon Byun, Youngjun Choi, Taero Kim, Sungrae Park, Kyungwoo SongAbstract
Pre-Layer Normalization (Pre-LN) is the de facto choice for large language models (LLMs) and is crucial for stable pretraining and effective transfer learning. However, Pre-LN is inefficient due to repeated statistical calculations and suffers from the curse of depth. As layers grow, the magnitude and variance of the hidden state escalate, destabilizing training. Efficiency-oriented normalization-free methods such as Dynamic Tanh (DyT) improve speed but remain fragile at depth. To jointly address stability and efficiency, we propose Bounded Hyperbolic Tanh (BHyT), a drop-in replacement for Pre-LN. BHyT couples a tanh nonlinearity with explicit, data-driven input bounding to keep activations within a non-saturating range. It prevents depth-wise growth in activation magnitude and variance and comes with a theoretical stability guarantee. For efficiency, BHyT computes exact statistics once per block and replaces a second normalization with a lightweight variance approximation, enhancing efficiency. Empirically, BHyT demonstrates improved stability and efficiency during pretraining, achieving an average of 15.8% faster training and an average of 4.2% higher token generation throughput compared to RMSNorm., while matching or surpassing its inference performance and robustness across language understanding and reasoning benchmarks. Our code is available at: this https URL
Abstract (translated)
预层归一化（Pre-LN）是大规模语言模型（LLMs）的默认选择，对于稳定的预训练和有效的迁移学习至关重要。然而，由于重复统计计算的存在，Pre-LN在效率方面表现不佳，并且随着网络深度增加会遇到“深度诅咒”的问题。随着层数的增长，隐藏状态的幅度和方差也会增大，从而导致训练变得不稳定。为了解决这一问题，一些专注于提高速度的方法如动态Tanh（DyT）虽然提高了计算效率，但在深层模型中的稳定性仍然较差。 为了同时解决稳定性和效率的问题，我们提出了有界双曲正切函数(Bounded Hyperbolic Tanh, BHyT)，这是一种可直接替换Pre-LN的方案。BHyT通过结合tanh非线性函数和基于数据驱动的显式输入边界来确保激活值保持在非饱和范围内，从而防止了随着深度增加而产生的幅度和方差增长，并提供了一个理论上的稳定性保证。 为了提高效率，BHyT每个块只计算一次精确统计量，并用一个轻量级的方差近似替换第二个归一化步骤，以进一步提升效率。实验证明，在预训练过程中，与RMSNorm相比，BHyT在保持或超越其推理性能和健壮性的同时，实现了平均15.8%更快的训练速度以及4.2%更高的标记生成吞吐量。 我们的代码可以在[此处](this https URL)获取。
URL
https://arxiv.org/abs/2601.09719
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Zero-Shot to Zero-Lies: Detecting Bengali Deepfake Audio through Transfer Learning
2025-12-25 14:53:40Most. Sharmin Sultana Samu, Md. Rakibul Islam, Md. Zahid Hossain, Md. Kamrozzaman Bhuiyan, Farhad Uz ZamanAbstract
The rapid growth of speech synthesis and voice conversion systems has made deepfake audio a major security concern. Bengali deepfake detection remains largely unexplored. In this work, we study automatic detection of Bengali audio deepfakes using the BanglaFake dataset. We evaluate zeroshot inference with several pretrained models. These include Wav2Vec2-XLSR-53, Whisper, PANNsCNN14, WavLM and Audio Spectrogram Transformer. Zero-shot results show limited detection ability. The best model, Wav2Vec2-XLSR-53, achieves 53.80% accuracy, 56.60% AUC and 46.20% EER. We then f ine-tune multiple architectures for Bengali deepfake detection. These include Wav2Vec2-Base, LCNN, LCNN-Attention, ResNet18, ViT-B16 and CNN-BiLSTM. Fine-tuned models show strong performance gains. ResNet18 achieves the highest accuracy of 79.17%, F1 score of 79.12%, AUC of 84.37% and EER of 24.35%. Experimental results confirm that fine-tuning significantly improves performance over zero-shot inference. This study provides the first systematic benchmark of Bengali deepfake audio detection. It highlights the effectiveness of f ine-tuned deep learning models for this low-resource language.
Abstract (translated)
语音合成和声音转换系统的迅速发展使得深度伪造音频成为了一个重要的安全问题。然而，孟加拉语的深度伪造检测仍是一个尚未充分探索的领域。在这项工作中，我们使用BanglaFake数据集研究了自动检测孟加拉语深度伪造音频的方法，并评估了几种预训练模型在零样本推理（zeroshot inference）下的性能表现。这些预训练模型包括Wav2Vec2-XLSR-53、Whisper、PANNsCNN14、WavLM和Audio Spectrogram Transformer。零样本推理的结果显示出有限的检测能力，其中最佳模型为Wav2Vec2-XLSR-53，其准确率为53.80%，AUC（Area Under Curve）值为56.60%，EER（等错误率）为46.20%。 接着，我们将多种架构进行了微调以专门用于孟加拉语深度伪造音频的检测。这些模型包括Wav2Vec2-Base、LCNN、LCNN-Attention、ResNet18、ViT-B16和CNN-BiLSTM。经过微调后的模型显示出了显著的性能提升，其中ResNet18取得了最佳的准确率79.17%，F1值为79.12%，AUC值为84.37%以及EER为24.35%。 实验结果证实，相对于零样本推理方法而言，微调策略极大地提高了性能。这项研究提供了孟加拉语深度伪造音频检测的第一个系统性基准测试，并强调了对于这种资源有限的语言来说，经过微调的深度学习模型的有效性。
URL
https://arxiv.org/abs/2512.21702
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ShrimpXNet: A Transfer Learning Framework for Shrimp Disease Classification with Augmented Regularization, Adversarial Training, and Explainable AI
2025-12-25 08:30:38Israk Hasan Jone, D. M. Rafiun Bin Masud, Promit Sarker, Sayed Fuad Al Labib, Nazmul Islam, Farhad BillahAbstract
Shrimp is one of the most widely consumed aquatic species globally, valued for both its nutritional content and economic importance. Shrimp farming represents a significant source of income in many regions; however, like other forms of aquaculture, it is severely impacted by disease outbreaks. These diseases pose a major challenge to sustainable shrimp production. To address this issue, automated disease classification methods can offer timely and accurate detection. This research proposes a deep learning-based approach for the automated classification of shrimp diseases. A dataset comprising 1,149 images across four disease classes was utilized. Six pretrained deep learning models, ResNet50, EfficientNet, DenseNet201, MobileNet, ConvNeXt-Tiny, and Xception were deployed and evaluated for performance. The images background was removed, followed by standardized preprocessing through the Keras image pipeline. Fast Gradient Sign Method (FGSM) was used for enhancing the model robustness through adversarial training. While advanced augmentation strategies, including CutMix and MixUp, were implemented to mitigate overfitting and improve generalization. To support interpretability, and to visualize regions of model attention, post-hoc explanation methods such as Grad-CAM, Grad-CAM++, and XGrad-CAM were applied. Exploratory results demonstrated that ConvNeXt-Tiny achieved the highest performance, attaining a 96.88% accuracy on the test dataset. After 1000 iterations, the 99% confidence interval for the model is [0.953,0.971].
Abstract (translated)
虾是全球消费最广泛的水生物种之一，因其营养价值和经济价值而备受重视。虾养殖在许多地区代表着重要的收入来源；然而，与其他形式的水产养殖一样，它严重受到疾病暴发的影响。这些疾病对可持续虾类生产构成了重大挑战。为了解决这个问题，自动化的疾病分类方法可以提供及时且准确的检测。本研究提出了一种基于深度学习的方法来实现虾病的自动化分类。该数据集包含跨越四个疾病的类别共1,149张图像。部署并评估了六种预训练的深度学习模型：ResNet50、EfficientNet、DenseNet201、MobileNet、ConvNeXt-Tiny和Xception。在Keras图像处理流水线中进行了背景移除及标准化预处理。使用快速梯度符号法（FGSM）通过对抗性训练来增强模型的鲁棒性。此外，采用了包括CutMix和MixUp在内的高级数据增强策略以减少过拟合并提高泛化能力。为了支持可解释性和可视化模型关注区域，应用了诸如Grad-CAM、Grad-CAM++和XGrad-CAM等事后解释方法。探索性结果显示，ConvNeXt-Tiny取得了最佳性能，在测试数据集上达到了96.88%的准确率。经过1000次迭代后，该模型在99%置信区间内的表现范围为[0.953, 0.971]。
URL
https://arxiv.org/abs/2601.00832
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Intelligent recognition of GPR road hidden defect images based on feature fusion and attention mechanism
2025-12-25 00:29:58Haotian Lv, Yuhui Zhang, Jiangbo Dai, Hanli Wu, Jiaji Wang, Dawei WangAbstract
Ground Penetrating Radar (GPR) has emerged as a pivotal tool for non-destructive evaluation of subsurface road defects. However, conventional GPR image interpretation remains heavily reliant on subjective expertise, introducing inefficiencies and inaccuracies. This study introduces a comprehensive framework to address these limitations: (1) A DCGAN-based data augmentation strategy synthesizes high-fidelity GPR images to mitigate data scarcity while preserving defect morphology under complex backgrounds; (2) A novel Multi-modal Chain and Global Attention Network (MCGA-Net) is proposed, integrating Multi-modal Chain Feature Fusion (MCFF) for hierarchical multi-scale defect representation and Global Attention Mechanism (GAM) for context-aware feature enhancement; (3) MS COCO transfer learning fine-tunes the backbone network, accelerating convergence and improving generalization. Ablation and comparison experiments validate the framework's efficacy. MCGA-Net achieves Precision (92.8%), Recall (92.5%), and mAP@50 (95.9%). In the detection of Gaussian noise, weak signals and small targets, MCGA-Net maintains robustness and outperforms other models. This work establishes a new paradigm for automated GPR-based defect detection, balancing computational efficiency with high accuracy in complex subsurface environments.
Abstract (translated)
地面穿透雷达（GPR）已成为非破坏性评估道路地下缺陷的重要工具。然而，传统的GPR图像解读仍然严重依赖于主观的专业知识，导致效率低下和不准确的问题。本研究提出了一套全面的框架来解决这些问题：(1) 一种基于DCGAN的数据增强策略可以合成高保真的GPR图像，以缓解数据稀缺问题，并在复杂背景下保持缺陷形态；(2) 提出了一个新颖的多模态链全球注意力网络（MCGA-Net），它整合了多模态链特征融合（MCFF）用于层次化的多尺度缺陷表示以及全局注意力机制（GAM）来增强上下文感知特性；(3) 利用MS COCO迁移学习微调骨干网络，以加速收敛并提高泛化能力。消融和对比实验验证了该框架的有效性。MCGA-Net在精度、召回率及mAP@50指标上分别达到了92.8%、92.5% 和 95.9%。在检测高斯噪声、弱信号以及小目标时，MCGA-Net保持了鲁棒性和性能优势。这项工作为基于GPR的自动缺陷检测建立了一个新的范式，在复杂的地下环境中实现了计算效率和高精度之间的平衡。
URL
https://arxiv.org/abs/2512.21452
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Graph Attention-based Adaptive Transfer Learning for Link Prediction
2025-12-24 05:11:34Huashen Lu, Wensheng Gan, Guoting Chen, Zhichao Huang, Philip S. YuAbstract
Graph neural networks (GNNs) have brought revolutionary advancements to the field of link prediction (LP), providing powerful tools for mining potential relationships in graphs. However, existing methods face challenges when dealing with large-scale sparse graphs and the need for a high degree of alignment between different datasets in transfer learning. Besides, although self-supervised methods have achieved remarkable success in many graph tasks, prior research has overlooked the potential of transfer learning to generalize across different graph datasets. To address these limitations, we propose a novel Graph Attention Adaptive Transfer Network (GAATNet). It combines the advantages of pre-training and fine-tuning to capture global node embedding information across datasets of different scales, ensuring efficient knowledge transfer and improved LP performance. To enhance the model's generalization ability and accelerate training, we design two key strategies: 1) Incorporate distant neighbor embeddings as biases in the self-attention module to capture global features. 2) Introduce a lightweight self-adapter module during fine-tuning to improve training efficiency. Comprehensive experiments on seven public datasets demonstrate that GAATNet achieves state-of-the-art performance in LP tasks. This study provides a general and scalable solution for LP tasks to effectively integrate GNNs with transfer learning. The source code and datasets are publicly available at this https URL
Abstract (translated)
图神经网络（GNNs）在链接预测（LP）领域带来了革命性的进步，为挖掘图形中潜在的关系提供了强大的工具。然而，现有的方法在处理大规模稀疏图和迁移学习中不同数据集之间高度对齐的需求时面临挑战。此外，尽管自监督方法在许多图任务上取得了显著成功，但之前的研究所忽视了迁移学习跨不同图数据集推广的潜力。为了解决这些限制，我们提出了一种新颖的图注意力适应性传输网络（GAATNet）。它结合预训练和微调的优势，捕获不同规模的数据集中节点嵌入信息的整体特性，确保有效的知识传递并提高LP性能。为了增强模型的泛化能力和加速训练过程，我们设计了两个关键策略：1）在自注意模块中将远距离邻居嵌入作为偏差以捕捉全局特征；2）在微调过程中引入一个轻量级自我适配器模块来提升训练效率。在七个公共数据集上的全面实验表明，GAATNet在LP任务上实现了最先进的性能。这项研究为LP任务提供了一种通用且可扩展的解决方案，能够有效地将GNNs与迁移学习相结合。源代码和数据集可以在以下网址获得：[此处应插入URL]。
URL
https://arxiv.org/abs/2512.22252
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AI-Driven Green Cognitive Radio Networks for Sustainable 6G Communication
2025-12-23 19:50:13Anshul Sharma, Shujaatali Badami, Biky Chouhan, Pushpanjali Pandey, Brijeena Rana, Navneet KaurAbstract
The 6G wireless aims at the Tb/s peak data rates are expected, a sub-millisecond latency, massive Internet of Things/vehicle connectivity, which requires sustainable access to audio over the air and energy-saving functionality. Cognitive Radio Networks CCNs help in alleviating the problem of spectrum scarcity, but classical sensing and allocation are still energy-consumption intensive, and sensitive to rapid spectrum variations. Our framework which centers on AI driven green CRN aims at integrating deep reinforcement learning (DRL) with transfer learning, energy harvesting (EH), reconfigurable intelligent surfaces (RIS) with other light-weight genetic refinement operations that optimally combine sensing timelines, transmit power, bandwidth distribution and RIS phase selection. Compared to two baselines, the utilization of MATLAB + NS-3 under dense loads, a traditional CRN with energy sensing under fixed policies, and a hybrid CRN with cooperative sensing under heuristic distribution of resource, there are (25-30%) fewer energy reserves used, sensing AUC greater than 0.90 and +6-13 p.p. higher PDR. The integrated framework is easily scalable to large IoT and vehicular applications, and it provides a feasible and sustainable roadmap to 6G CRNs. Index Terms--Cognitive Radio Networks (CRNs), 6G, Green Communication, Energy Efficiency, Deep Reinforcement Learning (DRL), Spectrum Sensing, RIS, Energy Harvesting, QoS, IoT.
Abstract (translated)
6G无线技术的目标是实现每秒太比特级别的峰值数据速率，亚毫秒级的延迟以及大规模物联网和车辆连接的需求，这需要持续的空中音频传输能力和节能功能。认知无线电网络（CRNs）有助于缓解频谱短缺的问题，但传统的感知和分配方法仍然消耗大量能量，并且对快速变化的频谱敏感。我们的框架以人工智能驱动的绿色CRN为中心，旨在将深度强化学习（DRL）与迁移学习、能量收集（EH）、可重构智能表面前沿技术以及其他轻量级遗传优化操作结合起来，这些操作能够最优地组合感知时间线、传输功率、带宽分配和RIS相位选择。 相较于两种基准方法——在高负载下使用MATLAB+NS-3的环境下的传统CRN频谱感知（固定策略）和混合CRN合作感知（基于启发式资源分布），我们的框架使用的能量储备减少了25%-30%，AUC值大于0.90，PDR提高了6-13个百分点。该综合框架易于扩展到大规模物联网和车辆应用，并为实现可持续发展的6G CRNs提供了可行的路线图。 关键词——认知无线电网络（CRN）、6G、绿色通信、能效、深度强化学习（DRL）、频谱感知、RIS、能量收集、服务质量（QoS）、物联网。
URL
https://arxiv.org/abs/2512.20739
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Skin Lesion Classification Using a Soft Voting Ensemble of Convolutional Neural Networks
2025-12-23 15:20:47Abdullah Al Shafi, Abdul Muntakim, Pintu Chandra Shill, Rowzatul Zannat, Abdullah Al-AminAbstract
Skin cancer can be identified by dermoscopic examination and ocular inspection, but early detection significantly increases survival chances. Artificial intelligence (AI), using annotated skin images and Convolutional Neural Networks (CNNs), improves diagnostic accuracy. This paper presents an early skin cancer classification method using a soft voting ensemble of CNNs. In this investigation, three benchmark datasets, namely HAM10000, ISIC 2016, and ISIC 2019, were used. The process involved rebalancing, image augmentation, and filtering techniques, followed by a hybrid dual encoder for segmentation via transfer learning. Accurate segmentation focused classification models on clinically significant features, reducing background artifacts and improving accuracy. Classification was performed through an ensemble of MobileNetV2, VGG19, and InceptionV3, balancing accuracy and speed for real-world deployment. The method achieved lesion recognition accuracies of 96.32\%, 90.86\%, and 93.92\% for the three datasets. The system performance was evaluated using established skin lesion detection metrics, yielding impressive results.
Abstract (translated)
皮肤癌可以通过皮肤镜检查和肉眼观察来识别，但早期检测可以显著提高生存率。人工智能（AI）通过使用标注的皮肤图像和卷积神经网络（CNNs），提高了诊断准确性。本文提出了一种利用软投票集成卷积神经网络进行早期皮肤癌分类的方法。在本研究中，使用了三个基准数据集，分别是HAM10000、ISIC 2016 和 ISIC 2019。该过程包括重新平衡、图像增强和过滤技术，随后通过迁移学习采用混合双编码器进行分割。准确的分割使分类模型聚焦于临床上重要的特征，减少了背景伪影并提高了准确性。通过MobileNetV2、VGG19 和 InceptionV3 的集成进行分类，在保证精度的同时也考虑了速度，以适应实际部署需求。该方法在三个数据集上分别实现了0.9632、0.9086和0.9392的病灶识别准确率。系统性能通过既定的皮肤病变检测指标进行了评估，取得了令人印象深刻的结果。
URL
https://arxiv.org/abs/2512.20431
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CLIP Based Region-Aware Feature Fusion for Automated BBPS Scoring in Colonoscopy Images
2025-12-23 13:58:12Yujia Fu, Zhiyu Dong, Tianwen Qian, Chenye Zheng, Danian Ji, Linhai ZhuoAbstract
Accurate assessment of bowel cleanliness is essential for effective colonoscopy procedures. The Boston Bowel Preparation Scale (BBPS) offers a standardized scoring system but suffers from subjectivity and inter-observer variability when performed manually. In this paper, to support robust training and evaluation, we construct a high-quality colonoscopy dataset comprising 2,240 images from 517 subjects, annotated with expert-agreed BBPS scores. We propose a novel automated BBPS scoring framework that leverages the CLIP model with adapter-based transfer learning and a dedicated fecal-feature extraction branch. Our method fuses global visual features with stool-related textual priors to improve the accuracy of bowel cleanliness evaluation without requiring explicit segmentation. Extensive experiments on both our dataset and the public NERTHU dataset demonstrate the superiority of our approach over existing baselines, highlighting its potential for clinical deployment in computer-aided colonoscopy analysis.
Abstract (translated)
准确的肠道清洁度评估对于有效的结肠镜检查至关重要。波士顿肠道准备评分量表（BBPS）提供了一种标准化评分系统，但在手动操作时容易受到主观性和观察者间差异的影响。在本文中，为了支持稳健的训练和评估，我们构建了一个高质量的结肠镜图像数据集，该数据集包含来自517名受试者的2,240张图像，并且这些图像已经由专家商定并标注了BBPS评分。我们提出了一种新的自动化BBPS评分框架，利用CLIP模型结合基于适配器的迁移学习和专门的粪便特征提取分支。我们的方法将全局视觉特征与与粪便相关的文本先验知识相结合，以提高肠道清洁度评估的准确性，并且无需显式分割操作。在我们自己的数据集以及公开的NERTHU数据集上的广泛实验表明，我们的方法优于现有的基线方法，突显了其在计算机辅助结肠镜检查分析中的临床应用潜力。
URL
https://arxiv.org/abs/2512.20374
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Clustering-based Transfer Learning for Dynamic Multimodal MultiObjective Evolutionary Algorithm
2025-12-22 01:51:26Li Yan, Bolun Liu, Chao Li, Jing Liang, Kunjie Yu, Caitong Yue, Xuzhao Chai, Boyang QuAbstract
Dynamic multimodal multiobjective optimization presents the dual challenge of simultaneously tracking multiple equivalent pareto optimal sets and maintaining population diversity in time-varying environments. However, existing dynamic multiobjective evolutionary algorithms often neglect solution modality, whereas static multimodal multiobjective evolutionary algorithms lack adaptability to dynamic changes. To address above challenge, this paper makes two primary contributions. First, we introduce a new benchmark suite of dynamic multimodal multiobjective test functions constructed by fusing the properties of both dynamic and multimodal optimization to establish a rigorous evaluation platform. Second, we propose a novel algorithm centered on a Clustering-based Autoencoder prediction dynamic response mechanism, which utilizes an autoencoder model to process matched clusters to generate a highly diverse initial population. Furthermore, to balance the algorithm's convergence and diversity, we integrate an adaptive niching strategy into the static optimizer. Empirical analysis on 12 instances of dynamic multimodal multiobjective test functions reveals that, compared with several state-of-the-art dynamic multiobjective evolutionary algorithms and multimodal multiobjective evolutionary algorithms, our algorithm not only preserves population diversity more effectively in the decision space but also achieves superior convergence in the objective space.
Abstract (translated)
动态多模态多目标优化面临着在时间变化的环境中同时跟踪多个等效帕累托最优集和保持种群多样性的双重挑战。然而，现有的动态多目标进化算法往往忽视了解决方案的模式性，而静态多模态多目标进化算法则缺乏对动态变化的适应能力。为解决上述难题，本文做出了两项主要贡献。 首先，我们引入了一套新的动态多模态多目标测试函数基准集，该基准集通过融合动态优化和多模态优化的特点建立了严格的评估平台。 其次，我们提出了一种基于聚类自编码器预测机制的新型算法。此算法利用自编码模型处理匹配的聚类以生成高度多样化的初始群体，并且为了平衡算法的收敛性和多样性，在静态优化器中集成了一种适应性利基策略。 通过对12个动态多模态多目标测试函数实例进行实证分析，结果表明与几种最先进的动态多目标进化算法和多模态多目标进化算法相比，我们的算法不仅更有效地在决策空间中保持了种群多样性，还在目标空间的收敛性方面取得了卓越的表现。
URL
https://arxiv.org/abs/2512.18947
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Personalized Spiking Neural Networks with Ferroelectric Synapses for EEG Signal Processing
2025-12-22 01:09:24Nikhil Garg, Anxiong Song, Niklas Plessnig, Nathan Savoia, Laura B\'egon-LoursAbstract
Electroencephalography (EEG)-based brain-computer interfaces (BCIs) are strongly affected by non-stationary neural signals that vary across sessions and individuals, limiting the generalization of subject-agnostic models and motivating adaptive and personalized learning on resource-constrained platforms. Programmable memristive hardware offers a promising substrate for such post-deployment adaptation; however, practical realization is challenged by limited weight resolution, device variability, nonlinear programming dynamics, and finite device endurance. In this work, we show that spiking neural networks (SNNs) can be deployed on ferroelectric memristive synaptic devices for adaptive EEG-based motor imagery decoding under realistic device constraints. We fabricate, characterize, and model ferroelectric synapses. We evaluate a convolutional-recurrent SNN architecture under two complementary deployment strategies: (i) device-aware training using a ferroelectric synapse model, and (ii) transfer of software-trained weights followed by low-overhead on-device re-tuning. To enable efficient adaptation, we introduce a device-aware weight-update strategy in which gradient-based updates are accumulated digitally and converted into discrete programming events only when a threshold is exceeded, emulating nonlinear, state-dependent programming dynamics while reducing programming frequency. Both deployment strategies achieve classification performance comparable to state-of-the-art software-based SNNs. Furthermore, subject-specific transfer learning achieved by retraining only the final network layers improves classification accuracy. These results demonstrate that programmable ferroelectric hardware can support robust, low-overhead adaptation in spiking neural networks, opening a practical path toward personalized neuromorphic processing of neural signals.
Abstract (translated)
基于脑电图（EEG）的脑机接口（BCI）受到会随时间、个体变化的非平稳神经信号的影响，这对无主体特定模型的应用推广造成了限制，并促进了资源受限平台上自适应和个性化学习的发展。可编程忆阻器硬件为这种部署后的适应提供了有前景的基础；然而，在实际应用中面临着权重分辨率有限、器件变异性大、编程动力学非线性和设备耐久度有限等挑战。在这项工作中，我们展示了在铁电忆阻突触装置上可以部署脉冲神经网络（SNN），用于具有现实设备约束条件下的自适应EEG运动想象解码。我们制造并表征了铁电突触，并对其进行了建模。我们在两种互补的部署策略下评估了一种卷积-递归SNN架构：（i）使用铁电突触模型进行感知训练；以及（ii）将软件训练权重转移到设备上，然后通过低开销重新调优完成适应。为了实现高效的自适应调整，我们引入了一种感知权重更新策略，在此策略中，基于梯度的更新数字累加并在阈值被超过时才转换为离散编程事件，从而模拟非线性和状态依赖性编程动力学，并减少了编程频率。这两种部署方式均实现了与当前最佳软件SNN相媲美的分类性能。此外，通过仅重新训练最终网络层实现特定主体的学习转移能够提高分类准确性。这些结果表明，可编程铁电硬件能够在脉冲神经网络中支持稳健且低开销的适应性调整，为神经信号的人工智能处理开辟了一条实用途径。
URL
https://arxiv.org/abs/2601.00020
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Controllable Probabilistic Forecasting with Stochastic Decomposition Layers
2025-12-21 17:10:00John S. Schreck, William E. Chapman, Charlie Becker, David John Gagne II, Dhamma Kimpara, Nihanth Cherukuru, Judith Berner, Kirsten J. Mayer, Negin SobhaniAbstract
AI weather prediction ensembles with latent noise injection and optimized with the continuous ranked probability score (CRPS) have produced both accurate and well-calibrated predictions with far less computational cost compared with diffusion-based methods. However, current CRPS ensemble approaches vary in their training strategies and noise injection mechanisms, with most injecting noise globally throughout the network via conditional normalization. This structure increases training expense and limits the physical interpretability of the stochastic perturbations. We introduce Stochastic Decomposition Layers (SDL) for converting deterministic machine learning weather models into probabilistic ensemble systems. Adapted from StyleGAN's hierarchical noise injection, SDL applies learned perturbations at three decoder scales through latent-driven modulation, per-pixel noise, and channel scaling. When applied to WXFormer via transfer learning, SDL requires less than 2\% of the computational cost needed to train the baseline model. Each ensemble member is generated from a compact latent tensor (5 MB), enabling perfect reproducibility and post-inference spread adjustment through latent rescaling. Evaluation on 2022 ERA5 reanalysis shows ensembles with spread-skill ratios approaching unity and rank histograms that progressively flatten toward uniformity through medium-range forecasts, achieving calibration competitive with operational IFS-ENS. Multi-scale experiments reveal hierarchical uncertainty: coarse layers modulate synoptic patterns while fine layers control mesoscale variability. The explicit latent parameterization provides interpretable uncertainty quantification for operational forecasting and climate applications.
Abstract (translated)
AI天气预测集成模型通过隐式噪声注入并使用连续排名概率评分（CRPS）进行优化，与基于扩散的方法相比，在计算成本大幅降低的情况下仍能提供准确且校准良好的预测结果。然而，当前的CRPS集成方法在训练策略和噪声注入机制方面存在差异，大多数采用全局条件归一化方式在整个网络中注入噪声。这种结构增加了训练成本，并限制了随机扰动的物理可解释性。我们引入了随机分解层（SDL），用于将确定性的机器学习天气模型转换为概率集合系统。SDL借鉴了StyleGAN分层噪声注入的方法，通过潜在驱动调制、逐像素噪声和通道缩放，在解码器的三个尺度上应用学到的扰动。当应用于通过迁移学习方式的WXFormer时，SDL所需的计算成本仅为基线模型训练成本的大约2%。每个集合成员由一个紧凑的潜在张量（5MB）生成，这使得完全可重现且可通过潜在重缩放进行后推理扩散调整成为可能。在2022年ERA5再分析数据上的评估表明，所构建的集成系统在中程预报中的扩散-技能比接近于1，排名直方图逐渐趋于均匀化，实现了与操作级IFS-ENS相当的校准水平。多尺度实验揭示了分层不确定性：粗粒度层次调节大尺度天气模式，而细粒度层次控制中尺度变化。显式的潜在参数化提供了适用于操作预报和气候应用的可解释性不确定性量化方法。
URL
https://arxiv.org/abs/2512.18815
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Pretrained Battery Transformer : A battery life prediction foundation model
2025-12-21 08:07:19Ruifeng Tan, Weixiang Hong, Jia Li, Jiaqiang Huang, Tong-Yi ZhangAbstract
Early prediction of battery cycle life is essential for accelerating battery research, manufacturing, and deployment. Although machine learning methods have shown encouraging results, progress is hindered by data scarcity and heterogeneity arising from diverse aging conditions. In other fields, foundation models (FMs) trained on diverse datasets have achieved broad generalization through transfer learning, but no FMs have been reported for battery cycle life prediction yet. Here we present the Pretrained Battery Transformer (PBT), the first FM for battery life prediction, developed through domain-knowledge-encoded mixture-of-expert layers. Validated on the largest public battery life database, PBT learns transferable representations from 13 lithium-ion battery (LIB) datasets, outperforming existing models by an average of 19.8%. With transfer learning, PBT achieves state-of-the-art performance across 15 diverse datasets encompassing various operating conditions, formation protocols, and chemistries. This work establishes a foundation model pathway for battery lifetime prediction, paving the way toward universal battery lifetime prediction systems.
Abstract (translated)
电池循环寿命的早期预测对于加速电池研究、制造和部署至关重要。尽管机器学习方法已经取得了令人鼓舞的结果，但由于不同老化条件导致的数据稀缺性和异质性，这一领域的进步受到了阻碍。在其他领域，通过多样化的数据集训练的基础模型（FMs）已通过迁移学习实现了广泛泛化，但尚未有关于电池循环寿命预测的基础模型的报道。在这里，我们介绍了预训练电池变换器（PBT），这是首个用于电池寿命预测的基础模型，它是通过编码领域知识的专家混合层开发出来的。在最大的公共电池寿命数据库上进行了验证，PBT从13个锂离子电池(LIB)数据集中学习可转移表示形式，在性能上比现有模型平均高出19.8%。利用迁移学习，PBT在包含各种工作条件、形成协议和化学成分的15种多样化的数据集上实现了最先进的性能。这项研究为电池寿命预测的基础模型路径奠定了基础，并开辟了向通用电池寿命预测系统发展的道路。
URL
https://arxiv.org/abs/2512.16334
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Unifying Causal Reinforcement Learning: Survey, Taxonomy, Algorithms and Applications
2025-12-19 23:37:22Cristiano da Costa Cunha, Wei Liu, Tim French, Ajmal MianAbstract
Integrating causal inference (CI) with reinforcement learning (RL) has emerged as a powerful paradigm to address critical limitations in classical RL, including low explainability, lack of robustness and generalization failures. Traditional RL techniques, which typically rely on correlation-driven decision-making, struggle when faced with distribution shifts, confounding variables, and dynamic environments. Causal reinforcement learning (CRL), leveraging the foundational principles of causal inference, offers promising solutions to these challenges by explicitly modeling cause-and-effect relationships. In this survey, we systematically review recent advancements at the intersection of causal inference and RL. We categorize existing approaches into causal representation learning, counterfactual policy optimization, offline causal RL, causal transfer learning, and causal explainability. Through this structured analysis, we identify prevailing challenges, highlight empirical successes in practical applications, and discuss open problems. Finally, we provide future research directions, underscoring the potential of CRL for developing robust, generalizable, and interpretable artificial intelligence systems.
Abstract (translated)
将因果推断（CI）与强化学习（RL）相结合，已成为解决经典RL中关键限制的强大范式，包括低可解释性、缺乏鲁棒性和泛化失败。传统的RL技术通常依赖于基于相关性的决策制定，在面对分布变化、混淆变量和动态环境时表现不佳。因果强化学习（CRL），通过利用因果推断的基本原理，通过显式建模因果关系提供了有希望的解决方案。在这篇综述中，我们系统地回顾了因果推断与RL交汇处的最新进展。我们将现有的方法分为因果表示学习、反事实策略优化、离线因果RL、因果迁移学习和因果可解释性等类别。通过对这些领域的结构化分析，我们识别出当前面临的挑战，并突出其实用应用中的实证成功案例，同时讨论开放的问题。最后，我们提供了未来的研究方向，强调CRL在开发稳健、通用且可解释的人工智能系统方面的潜力。
URL
https://arxiv.org/abs/2512.18135
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A Dataset and Benchmarks for Atrial Fibrillation Detection from Electrocardiograms of Intensive Care Unit Patients
2025-12-19 19:51:00Sarah Nassar, Nooshin Maghsoodi, Sophia Mannina, Shamel Addas, Stephanie Sibley, Gabor Fichtinger, David Pichora, David Maslove, Purang Abolmaesumi, Parvin MousaviAbstract
Objective: Atrial fibrillation (AF) is the most common cardiac arrhythmia experienced by intensive care unit (ICU) patients and can cause adverse health effects. In this study, we publish a labelled ICU dataset and benchmarks for AF detection. Methods: We compared machine learning models across three data-driven artificial intelligence (AI) approaches: feature-based classifiers, deep learning (DL), and ECG foundation models (FMs). This comparison addresses a critical gap in the literature and aims to pinpoint which AI approach is best for accurate AF detection. Electrocardiograms (ECGs) from a Canadian ICU and the 2021 PhysioNet/Computing in Cardiology Challenge were used to conduct the experiments. Multiple training configurations were tested, ranging from zero-shot inference to transfer learning. Results: On average and across both datasets, ECG FMs performed best, followed by DL, then feature-based classifiers. The model that achieved the top F1 score on our ICU test set was ECG-FM through a transfer learning strategy (F1=0.89). Conclusion: This study demonstrates promising potential for using AI to build an automatic patient monitoring system. Significance: By publishing our labelled ICU dataset (LinkToBeAdded) and performance benchmarks, this work enables the research community to continue advancing the state-of-the-art in AF detection in the ICU.
Abstract (translated)
目标：心房颤动（AF）是重症监护病房（ICU）患者中最常见的心脏心律失常，并可能导致不良健康影响。本研究发布了一个标记的ICU数据集和用于检测AF的基准。 方法：我们比较了三种基于数据的人工智能（AI）方法中的机器学习模型：特征分类器、深度学习（DL）和心电图基础模型（FMs）。这种对比解决了文献中一个关键空白，并旨在确定哪种AI方法最适合准确地检测AF。实验使用了来自加拿大ICU的心电图（ECG）数据以及2021年PhysioNet/计算心脏病学挑战赛的数据进行。我们测试了多种训练配置，从零样本推理到迁移学习。 结果：在两个数据集的平均表现中，心电图FMs表现出最佳效果，其次是深度学习方法，最后是特征分类器。在我们的ICU测试集中获得最高F1分数的模型是通过转移学习策略进行的心电图-FM（F1=0.89）。 结论：本研究表明使用AI建立自动患者监测系统具有巨大潜力。 重要意义：通过发布标记的ICU数据集（链接待添加）和性能基准，这项工作使研究社区能够继续推进ICU中AF检测领域的最新技术水平。
URL
https://arxiv.org/abs/2512.18031
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AdaptPrompt: Parameter-Efficient Adaptation of VLMs for Generalizable Deepfake Detection
2025-12-19 16:06:03Yichen Jiang, Mohammed Talha Alam, Sohail Ahmed Khan, Duc-Tien Dang-Nguyen, Fakhri KarrayAbstract
Recent advances in image generation have led to the widespread availability of highly realistic synthetic media, increasing the difficulty of reliable deepfake detection. A key challenge is generalization, as detectors trained on a narrow class of generators often fail when confronted with unseen models. In this work, we address the pressing need for generalizable detection by leveraging large vision-language models, specifically CLIP, to identify synthetic content across diverse generative techniques. First, we introduce Diff-Gen, a large-scale benchmark dataset comprising 100k diffusion-generated fakes that capture broad spectral artifacts unlike traditional GAN datasets. Models trained on Diff-Gen demonstrate stronger cross-domain generalization, particularly on previously unseen image generators. Second, we propose AdaptPrompt, a parameter-efficient transfer learning framework that jointly learns task-specific textual prompts and visual adapters while keeping the CLIP backbone frozen. We further show via layer ablation that pruning the final transformer block of the vision encoder enhances the retention of high-frequency generative artifacts, significantly boosting detection accuracy. Our evaluation spans 25 challenging test sets, covering synthetic content generated by GANs, diffusion models, and commercial tools, establishing a new state-of-the-art in both standard and cross-domain scenarios. We further demonstrate the framework's versatility through few-shot generalization (using as few as 320 images) and source attribution, enabling the precise identification of generator architectures in closed-set settings.
Abstract (translated)
最近在图像生成领域的进展带来了高度逼真的合成媒体的广泛可用性，这增加了可靠深度伪造检测的难度。一个关键挑战在于模型泛化能力，即针对特定生成器训练的检测器在面对未见过的新模型时往往表现不佳。本文通过利用大型视觉-语言模型（特别是CLIP）来识别不同生成技术产生的合成内容，从而解决了对于具备广泛适用性的检测方法的需求。 首先，我们引入了Diff-Gen，这是一个大规模基准数据集，包含10万张由扩散模型生成的伪造图像，这些图像涵盖了广泛的光谱特征，不同于传统的GAN数据集。在Diff-Gen上训练的模型显示出更强的跨域泛化能力，尤其是在面对未见过的图像生成器时。 其次，我们提出了AdaptPrompt，这是一种参数高效的迁移学习框架，它同时学习任务特定的文本提示和视觉适配器，并保持CLIP骨干网络不变。通过层消融实验，我们发现修剪视觉编码器中的最终Transformer块可以增强高频生成特征的保留，从而显著提高检测准确性。 我们的评估涵盖了25个具有挑战性的测试集，包括由GAN、扩散模型及商用工具生成的合成内容，在标准和跨域场景中均建立了新的最先进的性能。此外，通过展示框架在少量样本泛化（使用最少320张图像）以及源归属方面的多功能性，我们证明了它能够准确识别封闭集合设置中的生成器架构。
URL
https://arxiv.org/abs/2512.17730
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HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
2025-12-19 12:58:06Christian Lagemann, Sajeda Mokbel, Miro Gondrum, Mario R\"uttgers, Jared Callaham, Ludger Paehler, Samuel Ahnert, Nicholas Zolman, Kai Lagemann, Nikolaus Adams, Matthias Meinke, Wolfgang Schr\"oder, Jean-Christophe Loiseau, Esther Lagemann, Steven L. BruntonAbstract
Modeling and controlling fluid flows is critical for several fields of science and engineering, including transportation, energy, and medicine. Effective flow control can lead to, e.g., lift increase, drag reduction, mixing enhancement, and noise reduction. However, controlling a fluid faces several significant challenges, including high-dimensional, nonlinear, and multiscale interactions in space and time. Reinforcement learning (RL) has recently shown great success in complex domains, such as robotics and protein folding, but its application to flow control is hindered by a lack of standardized benchmark platforms and the computational demands of fluid simulations. To address these challenges, we introduce HydroGym, a solver-independent RL platform for flow control research. HydroGym integrates sophisticated flow control benchmarks, scalable runtime infrastructure, and state-of-the-art RL algorithms. Our platform includes 42 validated environments spanning from canonical laminar flows to complex three-dimensional turbulent scenarios, validated over a wide range of Reynolds numbers. We provide non-differentiable solvers for traditional RL and differentiable solvers that dramatically improve sample efficiency through gradient-enhanced optimization. Comprehensive evaluation reveals that RL agents consistently discover robust control principles across configurations, such as boundary layer manipulation, acoustic feedback disruption, and wake reorganization. Transfer learning studies demonstrate that controllers learned at one Reynolds number or geometry adapt efficiently to new conditions, requiring approximately 50% fewer training episodes. The HydroGym platform is highly extensible and scalable, providing a framework for researchers in fluid dynamics, machine learning, and control to add environments, surrogate models, and control algorithms to advance science and technology.
Abstract (translated)
流体流动的建模和控制对于包括交通、能源和医学在内的多个科学与工程领域至关重要。有效的流动控制可以带来诸如升力增加、阻力减小、混合增强以及噪音减少等好处。然而，控制流体面临诸多挑战，其中包括高维性、非线性和空间时间多尺度交互作用。近年来，强化学习（RL）在复杂领域的应用如机器人技术和蛋白质折叠方面取得了显著成功，但其在流动控制中的应用受到缺乏标准化基准平台和流体模拟计算需求的限制。为了解决这些问题，我们引入了HydroGym——一个用于流动控制研究的独立求解器强化学习平台。HydroGym整合了复杂的流动控制基准测试、可扩展运行时基础设施以及先进的RL算法。我们的平台包括42个经过验证的环境，从经典的层流到复杂三维湍流场景不等，涵盖广泛的雷诺数范围。我们为传统的RL提供了非微分求解器，并提供可通过梯度增强优化显著提高样本效率的微分求解器。全面评估表明，强化学习代理能够一致地在各种配置中发现有效的控制原则，如边界层操作、声反馈干扰和尾流重组。迁移学习研究表明，在一个雷诺数或几何条件下学到的控制器可以高效适应新的条件，所需的训练回合大约减少了50%。HydroGym平台高度可扩展且具有规模性，为从事流体动力学、机器学习与控制研究的科研人员提供了一个框架，让他们能够添加环境、替代模型和控制算法，从而推进科学和技术的发展。
URL
https://arxiv.org/abs/2512.17534
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Convolutional-neural-operator-based transfer learning for solving PDEs
2025-12-19 03:55:20Peng Fan, Guofei PangAbstract
Convolutional neural operator is a CNN-based architecture recently proposed to enforce structure-preserving continuous-discrete equivalence and enable the genuine, alias-free learning of solution operators of PDEs. This neural operator was demonstrated to outperform for certain cases some baseline models such as DeepONet, Fourier neural operator, and Galerkin transformer in terms of surrogate accuracy. The convolutional neural operator, however, seems not to be validated for few-shot learning. We extend the model to few-shot learning scenarios by first pre-training a convolutional neural operator using a source dataset and then adjusting the parameters of the trained neural operator using only a small target dataset. We investigate three strategies for adjusting the parameters of a trained neural operator, including fine-tuning, low-rank adaption, and neuron linear transformation, and find that the neuron linear transformation strategy enjoys the highest surrogate accuracy in solving PDEs such as Kuramoto-Sivashinsky equation, Brusselator diffusion-reaction system, and Navier-Stokes equations.
Abstract (translated)
卷积神经算子是一种最近提出的基于CNN（卷积神经网络）的架构，旨在强制执行保持结构连续-离散等价性，并使PDE（偏微分方程）解算器的学习真正无混叠。这种神经算子在某些情况下展示出优于一些基准模型（如DeepONet、傅立叶神经算子和Galerkin变换器）的替代准确性。然而，卷积神经算子似乎尚未在少量样本学习中得到验证。我们通过首先使用源数据集对卷积神经算子进行预训练，然后仅利用小规模目标数据集调整已训练神经算子的参数的方式，将该模型扩展到了少量样本学习场景下。 为了调整已训练神经算子的参数，我们研究了三种策略：微调、低秩适应和神经元线性变换。实验结果表明，在求解柯特沃什尼-西瓦什金斯基方程（Kuramoto-Sivashinsky equation）、布鲁塞尔扩散反应系统（Brusselator diffusion-reaction system）以及纳维叶-斯托克斯方程（Navier-Stokes equations）等PDE时，神经元线性变换策略在替代准确性方面表现出最高的性能。
URL
https://arxiv.org/abs/2512.17969
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Skeleton-Snippet Contrastive Learning with Multiscale Feature Fusion for Action Localization
2025-12-18 13:15:52Qiushuo Cheng, Jingjing Liu, Catherine Morgan, Alan Whone, Majid MirmehdiAbstract
The self-supervised pretraining paradigm has achieved great success in learning 3D action representations for skeleton-based action recognition using contrastive learning. However, learning effective representations for skeleton-based temporal action localization remains challenging and underexplored. Unlike video-level {action} recognition, detecting action boundaries requires temporally sensitive features that capture subtle differences between adjacent frames where labels change. To this end, we formulate a snippet discrimination pretext task for self-supervised pretraining, which densely projects skeleton sequences into non-overlapping segments and promotes features that distinguish them across videos via contrastive learning. Additionally, we build on strong backbones of skeleton-based action recognition models by fusing intermediate features with a U-shaped module to enhance feature resolution for frame-level localization. Our approach consistently improves existing skeleton-based contrastive learning methods for action localization on BABEL across diverse subsets and evaluation protocols. We also achieve state-of-the-art transfer learning performance on PKUMMD with pretraining on NTU RGB+D and BABEL.
Abstract (translated)
自监督预训练范式在使用对比学习方法从基于骨架的动作识别中学习3D动作表示方面取得了巨大成功。然而，为基于骨架的时间动作定位学习有效的表示仍然具有挑战性且研究不足。与视频级动作识别不同，检测动作边界需要能够捕捉到标签变化前后相邻帧之间细微差别的时间敏感特征。为此，我们提出了一个片段区分的预训练任务，将骨架序列密集地投影到非重叠段中，并通过对比学习促进跨视频区分这些段落的特性。此外，我们在基于骨架的动作识别模型的强大骨干网络基础上融合了中间特征和U形模块以增强帧级定位的特征分辨率。我们的方法在BABEL数据集的不同子集和评估协议上持续改进现有的基于骨架的对比学习方法用于动作定位的表现。我们还在PKUMMD上实现了最先进的迁移学习性能，使用NTU RGB+D和BABEL进行预训练。
URL
https://arxiv.org/abs/2512.16504
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Comprehensive Deployment-Oriented Assessment for Cross-Environment Generalization in Deep Learning-Based mmWave Radar Sensing
2025-12-15 06:29:51Tomoya Tanaka, Tomonori Ikeda, Ryo YonemotoAbstract
This study presents the first comprehensive evaluation of spatial generalization techniques, which are essential for the practical deployment of deep learning-based radio-frequency (RF) sensing. Focusing on people counting in indoor environments using frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) radar, we systematically investigate a broad set of approaches, including amplitude-based statistical preprocessing (sigmoid weighting and threshold zeroing), frequency-domain filtering, autoencoder-based background suppression, data augmentation strategies, and transfer learning. Experimental results collected across two environments with different layouts demonstrate that sigmoid-based amplitude weighting consistently achieves superior cross-environment performance, yielding 50.1% and 55.2% reductions in root-mean-square error (RMSE) and mean absolute error (MAE), respectively, compared with baseline methods. Data augmentation provides additional though modest benefits, with improvements up to 8.8% in MAE. By contrast, transfer learning proves indispensable for large spatial shifts, achieving 82.1% and 91.3% reductions in RMSE and MAE, respectively, with 540 target-domain samples. Taken together, these findings establish a highly practical direction for developing radar sensing systems capable of maintaining robust accuracy under spatial variations by integrating deep learning models with amplitude-based preprocessing and efficient transfer learning.
Abstract (translated)
这项研究首次全面评估了空间泛化技术，这些技术对于基于深度学习的射频（RF）传感的实际部署至关重要。本研究专注于使用频率调制连续波（FMCW）多输入多输出（MIMO）雷达在室内环境中进行人员计数，并系统地调查了一整套方法，包括基于幅度的统计预处理（sigmoid加权和阈值置零）、频域滤波、基于自动编码器的背景抑制、数据增强策略以及迁移学习。 实验结果来自两个具有不同布局的环境，在这些环境中收集的数据表明，基于sigmoid的幅度加权在跨环境性能方面始终优于基线方法，分别将均方根误差（RMSE）和平均绝对误差（MAE）降低了50.1% 和 55.2%。数据增强虽然提供了额外但相对较小的好处，在平均绝对误差上最多提高了8.8%。相比之下，迁移学习对于大的空间变化是必不可少的，在仅有540个目标领域样本的情况下，分别实现了RMSE和MAE减少了82.1%和91.3%。 综合来看，这些发现确立了一条高度实用的方向，通过将深度学习模型与基于幅度的预处理以及高效的迁移学习相结合，开发出能够在空间变化下保持稳健准确性的雷达传感系统。
URL
https://arxiv.org/abs/2512.13018
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