Transfer_Learning
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Leveraging Convolutional Sparse Autoencoders for Robust Movement Classification from Low-Density sEMG
2026-01-30 14:21:46Blagoj Hristov, Zoran Hadzi-Velkov, Katerina Hadzi-Velkova Saneva, Gorjan Nadzinski, Vesna Ojleska LatkoskaAbstract
Reliable control of myoelectric prostheses is often hindered by high inter-subject variability and the clinical impracticality of high-density sensor arrays. This study proposes a deep learning framework for accurate gesture recognition using only two surface electromyography (sEMG) channels. The method employs a Convolutional Sparse Autoencoder (CSAE) to extract temporal feature representations directly from raw signals, eliminating the need for heuristic feature engineering. On a 6-class gesture set, our model achieved a multi-subject F1-score of 94.3% $\pm$ 0.3%. To address subject-specific differences, we present a few-shot transfer learning protocol that improved performance on unseen subjects from a baseline of 35.1% $\pm$ 3.1% to 92.3% $\pm$ 0.9% with minimal calibration data. Furthermore, the system supports functional extensibility through an incremental learning strategy, allowing for expansion to a 10-class set with a 90.0% $\pm$ 0.2% F1-score without full model retraining. By combining high precision with minimal computational and sensor overhead, this framework provides a scalable and efficient approach for the next generation of affordable and adaptive prosthetic systems.
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URL
https://arxiv.org/abs/2601.23011
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AI-Enabled Waste Classification as a Data-Driven Decision Support Tool for Circular Economy and Urban Sustainability
2026-01-30 00:10:40Julius Sechang Mboli, Omolara Aderonke OgungbemiAbstract
Efficient waste sorting is crucial for enabling circular-economy practices and resource recovery in smart cities. This paper evaluates both traditional machine-learning (Random Forest, SVM, AdaBoost) and deep-learning techniques including custom CNNs, VGG16, ResNet50, and three transfer-learning models (DenseNet121, EfficientNetB0, InceptionV3) for binary classification of 25 077 waste images (80/20 train/test split, augmented and resized to 150x150 px). The paper assesses the impact of Principal Component Analysis for dimensionality reduction on traditional models. DenseNet121 achieved the highest accuracy (91 %) and ROC-AUC (0.98), outperforming the best traditional classifier by 20 pp. Principal Component Analysis (PCA) showed negligible benefit for classical methods, whereas transfer learning substantially improved performance under limited-data conditions. Finally, we outline how these models integrate into a real-time Data-Driven Decision Support System for automated waste sorting, highlighting potential reductions in landfill use and lifecycle environmental impacts.)
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URL
https://arxiv.org/abs/2601.22418
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Abstracting Robot Manipulation Skills via Mixture-of-Experts Diffusion Policies
2026-01-29 04:17:56Ce Hao, Xuanran Zhai, Yaohua Liu, Harold SohAbstract
Diffusion-based policies have recently shown strong results in robot manipulation, but their extension to multi-task scenarios is hindered by the high cost of scaling model size and demonstrations. We introduce Skill Mixture-of-Experts Policy (SMP), a diffusion-based mixture-of-experts policy that learns a compact orthogonal skill basis and uses sticky routing to compose actions from a small, task-relevant subset of experts at each step. A variational training objective supports this design, and adaptive expert activation at inference yields fast sampling without oversized backbones. We validate SMP in simulation and on a real dual-arm platform with multi-task learning and transfer learning tasks, where SMP achieves higher success rates and markedly lower inference cost than large diffusion baselines. These results indicate a practical path toward scalable, transferable multi-task manipulation: learn reusable skills once, activate only what is needed, and adapt quickly when tasks change.
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URL
https://arxiv.org/abs/2601.21251
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Cross-Domain Offshore Wind Power Forecasting: Transfer Learning Through Meteorological Clusters
2026-01-27 14:54:27Dominic Weisser, Chlo\'e Hashimoto-Cullen, Benjamin GuedjAbstract
Ambitious decarbonisation targets are catalysing growth in orders of new offshore wind farms. For these newly commissioned plants to run, accurate power forecasts are needed from the onset. These allow grid stability, good reserve management and efficient energy trading. Despite machine learning models having strong performances, they tend to require large volumes of site-specific data that new farms do not yet have. To overcome this data scarcity, we propose a novel transfer learning framework that clusters power output according to covariate meteorological features. Rather than training a single, general-purpose model, we thus forecast with an ensemble of expert models, each trained on a cluster. As these pre-trained models each specialise in a distinct weather pattern, they adapt efficiently to new sites and capture transferable, climate-dependent dynamics. Through the expert models' built-in calibration to seasonal and meteorological variability, we remove the industry-standard requirement of local measurements over a year. Our contributions are two-fold - we propose this novel framework and comprehensively evaluate it on eight offshore wind farms, achieving accurate cross-domain forecasting with under five months of site-specific data. Our experiments achieve a MAE of 3.52\%, providing empirical verification that reliable forecasts do not require a full annual cycle. Beyond power forecasting, this climate-aware transfer learning method opens new opportunities for offshore wind applications such as early-stage wind resource assessment, where reducing data requirements can significantly accelerate project development whilst effectively mitigating its inherent risks.
Abstract (translated)
URL
https://arxiv.org/abs/2601.19674
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A General-Purpose Diversified 2D Seismic Image Dataset from NAMSS
2026-01-27 14:01:29Lucas de Magalh\~aes Araujo, Ot\'avio Oliveira Napoli, Sandra Avila, Edson BorinAbstract
We introduce the Unicamp-NAMSS dataset, a large, diverse, and geographically distributed collection of migrated 2D seismic sections designed to support modern machine learning research in geophysics. We constructed the dataset from the National Archive of Marine Seismic Surveys (NAMSS), which contains decades of publicly available marine seismic data acquired across multiple regions, acquisition conditions, and geological settings. After a comprehensive collection and filtering process, we obtained 2588 cleaned and standardized seismic sections from 122 survey areas, covering a wide range of vertical and horizontal sampling characteristics. To ensure reliable experimentation, we balanced the dataset so that no survey dominates the distribution, and partitioned it into non-overlapping macro-regions for training, validation, and testing. This region-disjoint split allows robust evaluation of generalization to unseen geological and acquisition conditions. We validated the dataset through quantitative and embedding-space analyses using both convolutional and transformer-based models. These analyses showed that Unicamp-NAMSS exhibits substantial variability within and across regions, while maintaining coherent structure across acquisition macro-region and survey types. Comparisons with widely used interpretation datasets (Parihaka and F3 Block) further demonstrated that Unicamp-NAMSS covers a broader portion of the seismic appearance space, making it a strong candidate for machine learning model pretraining. The dataset, therefore, provides a valuable resource for machine learning tasks, including self-supervised representation learning, transfer learning, benchmarking supervised tasks such as super-resolution or attribute prediction, and studying domain adaptation in seismic interpretation.
Abstract (translated)
我们介绍了Unicamp-NAMSS数据集，这是一个大型、多样且地理分布广泛的数据集合，包含迁移的二维地震剖面，旨在支持地球物理学领域的现代机器学习研究。该数据集由国家海洋地震调查档案（NAMSS）构建而成，NAMSS包含了数十年来在多个地区和地质条件下获得并公开的海上地震数据。 经过全面收集和过滤过程后，我们从122个调查区域中获得了2588份清洗和标准化后的地震剖面，涵盖了广泛的垂直和水平采样特性。为了确保可靠的实验结果，我们将该数据集进行了平衡处理，以保证任何单一调查都不会占据主导地位，并将其划分成互不重叠的宏区用于训练、验证和测试。这种区域分离的分割方式允许对模型在未见过地质和获取条件下的泛化能力进行稳健评估。 我们使用卷积和基于Transformer的模型通过定量分析及嵌入空间分析对该数据集进行了验证，结果显示Unicamp-NAMSS在不同地区之间以及各个内部都表现出显著的变化性，并且在整个采集宏区和调查类型上保持了连贯的结构。与广泛使用的解释数据集（Parihaka和F3区块）进行对比进一步表明，Unicamp-NAMSS覆盖了更广泛的地震外观空间部分，使其成为机器学习模型预训练的一个强有力候选者。 因此，该数据集为包括自我监督表征学习、迁移学习、基准测试如超分辨率或属性预测任务以及研究地震解释中的领域适应性在内的各种机器学习任务提供了一个宝贵的资源。
URL
https://arxiv.org/abs/2602.04890
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Comparative Evaluation of Machine Learning Algorithms for Affective State Recognition from Children's Drawings
2026-01-26 12:12:24Aura Loredana DanAbstract
Autism spectrum disorder (ASD) represents a neurodevelopmental condition characterized by difficulties in expressing emotions and communication, particularly during early childhood. Understanding the affective state of children at an early age remains challenging, as conventional assessment methods are often intrusive, subjective, or difficult to apply consistently. This paper builds upon previous work on affective state recognition from children's drawings by presenting a comparative evaluation of machine learning models for emotion classification. Three deep learning architectures -- MobileNet, EfficientNet, and VGG16 -- are evaluated within a unified experimental framework to analyze classification performance, robustness, and computational efficiency. The models are trained using transfer learning on a dataset of children's drawings annotated with emotional labels provided by psychological experts. The results highlight important trade-offs between lightweight and deeper architectures when applied to drawing-based affective computing tasks, particularly in mobile and real-time application contexts.
Abstract (translated)
URL
https://arxiv.org/abs/2601.18414
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LungCRCT: Causal Representation based Lung CT Processing for Lung Cancer Treatment
2026-01-26 04:03:50Daeyoung KimAbstract
Due to silence in early stages, lung cancer has been one of the most leading causes of mortality in cancer patients world-wide. Moreover, major symptoms of lung cancer are hard to differentiate with other respiratory disease symptoms such as COPD, further leading patients to overlook cancer progression in early stages. Thus, to enhance survival rates in lung cancer, early detection from consistent proactive respiratory system monitoring becomes crucial. One of the most prevalent and effective methods for lung cancer monitoring would be low-dose computed tomography(LDCT) chest scans, which led to remarkable enhancements in lung cancer detection or tumor classification tasks under rapid advancements and applications of computer vision based AI models such as EfficientNet or ResNet in image processing. However, though advanced CNN models under transfer learning or ViT based models led to high performing lung cancer detections, due to its intrinsic limitations in terms of correlation dependence and low interpretability due to complexity, expansions of deep learning models to lung cancer treatment analysis or causal intervention analysis simulations are still limited. Therefore, this research introduced LungCRCT: a latent causal representation learning based lung cancer analysis framework that retrieves causal representations of factors within the physical causal mechanism of lung cancer progression. With the use of advanced graph autoencoder based causal discovery algorithms with distance Correlation disentanglement and entropy-based image reconstruction refinement, LungCRCT not only enables causal intervention analysis for lung cancer treatments, but also leads to robust, yet extremely light downstream models in malignant tumor classification tasks with an AUC score of 93.91%.
Abstract (translated)
URL
https://arxiv.org/abs/2601.18118
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Cross-Domain Transfer with Self-Supervised Spectral-Spatial Modeling for Hyperspectral Image Classification
2026-01-26 02:52:35Jianshu Chao, Tianhua Lv, Qiqiong Ma, Yunfei Qiu, Li Fang, Huifang Shen, Wei YaoAbstract
Self-supervised learning has demonstrated considerable potential in hyperspectral representation, yet its application in cross-domain transfer scenarios remains under-explored. Existing methods, however, still rely on source domain annotations and are susceptible to distribution shifts, leading to degraded generalization performance in the target domain. To address this, this paper proposes a self-supervised cross-domain transfer framework that learns transferable spectral-spatial joint representations without source labels and achieves efficient adaptation under few samples in the target domain. During the self-supervised pre-training phase, a Spatial-Spectral Transformer (S2Former) module is designed. It adopts a dual-branch spatial-spectral transformer and introduces a bidirectional cross-attention mechanism to achieve spectral-spatial collaborative modeling: the spatial branch enhances structural awareness through random masking, while the spectral branch captures fine-grained differences. Both branches mutually guide each other to improve semantic consistency. We further propose a Frequency Domain Constraint (FDC) to maintain frequency-domain consistency through real Fast Fourier Transform (rFFT) and high-frequency magnitude loss, thereby enhancing the model's capability to discern fine details and boundaries. During the fine-tuning phase, we introduce a Diffusion-Aligned Fine-tuning (DAFT) distillation mechanism. This aligns semantic evolution trajectories through a teacher-student structure, enabling robust transfer learning under low-label conditions. Experimental results demonstrate stable classification performance and strong cross-domain adaptability across four hyperspectral datasets, validating the method's effectiveness under resource-constrained conditions.
Abstract (translated)
URL
https://arxiv.org/abs/2601.18088
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A Novel Transfer Learning Approach for Mental Stability Classification from Voice Signal
2026-01-23 14:45:34Rafiul Islam, Md. Taimur AhadAbstract
This study presents a novel transfer learning approach and data augmentation technique for mental stability classification using human voice signals and addresses the challenges associated with limited data availability. Convolutional neural networks (CNNs) have been employed to analyse spectrogram images generated from voice recordings. Three CNN architectures, VGG16, InceptionV3, and DenseNet121, were evaluated across three experimental phases: training on non-augmented data, augmented data, and transfer learning. This proposed transfer learning approach involves pre-training models on the augmented dataset and fine-tuning them on the non-augmented dataset while ensuring strict data separation to prevent data leakage. The results demonstrate significant improvements in classification performance compared to the baseline approach. Among three CNN architectures, DenseNet121 achieved the highest accuracy of 94% and an AUC score of 99% using the proposed transfer learning approach. This finding highlights the effectiveness of combining data augmentation and transfer learning to enhance CNN-based classification of mental stability using voice spectrograms, offering a promising non-invasive tool for mental health diagnostics.
Abstract (translated)
URL
https://arxiv.org/abs/2601.16793
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Transfer Learning from ImageNet for MEG-Based Decoding of Imagined Speech
2026-01-22 12:38:20Soufiane Jhilal, St\'ephanie Martin, Anne-Lise GiraudAbstract
Non-invasive decoding of imagined speech remains challenging due to weak, distributed signals and limited labeled data. Our paper introduces an image-based approach that transforms magnetoencephalography (MEG) signals into time-frequency representations compatible with pretrained vision models. MEG data from 21 participants performing imagined speech tasks were projected into three spatial scalogram mixtures via a learnable sensor-space convolution, producing compact image-like inputs for ImageNet-pretrained vision architectures. These models outperformed classical and non-pretrained models, achieving up to 90.4% balanced accuracy for imagery vs. silence, 81.0% vs. silent reading, and 60.6% for vowel decoding. Cross-subject evaluation confirmed that pretrained models capture shared neural representations, and temporal analyses localized discriminative information to imagery-locked intervals. These findings show that pretrained vision models applied to image-based MEG representations can effectively capture the structure of imagined speech in non-invasive neural signals.
Abstract (translated)
想象中的言语非侵入性解码由于微弱且分布广泛的信号以及有限的标注数据而具有挑战性。我们的论文介绍了一种基于图像的方法，该方法将脑磁图（MEG）信号转换为与预训练视觉模型兼容的时间-频率表示形式。21名参与者在执行想象说话任务时采集的MEG数据通过可学习的传感器空间卷积被投影到三个时空频谱混合体中，产生了适合ImageNet预训练视觉架构的紧凑图像样输入。这些模型超越了传统和未经过预训练的模型，在想象言语与静默之间的分类中达到了最高90.4%的平衡准确率；在想象言语与无声阅读之间达到了81.0%的准确率；以及60.6%的元音解码准确性。跨主体评估证实，预训练模型能够捕捉到共享的神经表示，并且时间分析将区分信息定位到了与想象说话相关的特定时间段内。这些发现表明，当应用于基于图像的MEG表示时，预先训练的视觉模型可以有效地捕获非侵入性神经信号中所含有的想象言语结构。
URL
https://arxiv.org/abs/2601.15909
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A Machine Vision Approach to Preliminary Skin Lesion Assessments
2026-01-21 23:48:59Ali Khreis, Ro'Yah Radaideh, Quinn McGillAbstract
Early detection of malignant skin lesions is critical for improving patient outcomes in aggressive, metastatic skin cancers. This study evaluates a comprehensive system for preliminary skin lesion assessment that combines the clinically established ABCD rule of dermoscopy (analyzing Asymmetry, Borders, Color, and Dermoscopic Structures) with machine learning classification. Using a 1,000-image subset of the HAM10000 dataset, the system implements an automated, rule-based pipeline to compute a Total Dermoscopy Score (TDS) for each lesion. This handcrafted approach is compared against various machine learning solutions, including traditional classifiers (Logistic Regression, Random Forest, and SVM) and deep learning models. While the rule-based system provides high clinical interpretability, results indicate a performance bottleneck when reducing complex morphology to five numerical features. Experimental findings show that transfer learning with EfficientNet-B0 failed significantly due to domain shift between natural and medical images. In contrast, a custom three-layer Convolutional Neural Network (CNN) trained from scratch achieved 78.5% accuracy and 86.5% recall on median-filtered images, representing a 19-point accuracy improvement over traditional methods. The results demonstrate that direct pixel-level learning captures diagnostic patterns beyond handcrafted features and that purpose-built lightweight architectures can outperform large pretrained models for small, domain-specific medical datasets.
Abstract (translated)
早期发现恶性皮肤病变对于改善侵袭性和转移性皮肤癌患者的预后至关重要。本研究评估了一种综合系统，用于初步皮肤病变的筛查，该系统结合了临床公认的皮肤病学ABC德摩斯科检查规则（分析不对称性、边界、颜色和皮肤病结构）与机器学习分类方法。利用HAM10000数据集的一个包含1,000张图像的子集，该系统实施了一个自动化的基于规则的工作流程来计算每个病变的总皮肤镜评分（TDS）。这种方法被对比了各种机器学习解决方案，包括传统的分类器（逻辑回归、随机森林和支持向量机）和深度学习模型。尽管基于规则的方法提供了高临床可解释性，但结果表明在将复杂形态简化为五个数值特征时存在性能瓶颈。 实验发现表明，在自然图像和医学图像之间进行领域转换使得使用EfficientNet-B0的迁移学习失败。相比之下，一个定制的三层卷积神经网络（CNN），从零开始训练，在中值滤波后的图像上达到了78.5%的准确率和86.5%的召回率，比传统方法在准确性方面提高了19个百分点。这些结果表明直接基于像素的学习能够捕捉到超越手工制作特征的诊断模式，并且为小规模、领域特定的医学数据集量身定制的轻量级架构可以胜过大型预训练模型。 总的来说，这项研究强调了结合临床专业知识和机器学习技术在提高皮肤病变检测准确性方面的重要性。
URL
https://arxiv.org/abs/2601.15539
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The Dark Side of AI Transformers: Sentiment Polarization & the Loss of Business Neutrality by NLP Transformers
Abstract
The use of Transfer Learning & Transformers has steadily improved accuracy and has significantly contributed in solving complex computation problems. However, this transformer led accuracy improvement in Applied AI Analytics specifically in sentiment analytics comes with the dark side. It is observed during experiments that a lot of these improvements in transformer led accuracy of one class of sentiment has been at the cost of polarization of another class of sentiment and the failing of neutrality. This lack of neutrality poses an acute problem in the Applied NLP space, which relies heavily on the computational outputs of sentiment analytics for reliable industry ready tasks.
Abstract (translated)
使用迁移学习和Transformer技术稳步提高了准确性，并在解决复杂计算问题方面做出了重大贡献。然而，在应用人工智能分析，特别是在情感分析中，基于Transformer的准确率提升也带来了一个阴暗面。实验观察发现，一个类别的情感准确率提高往往是以另一个类别的情感极化为代价，导致中立性的缺失。这种缺乏中立性在依赖情感分析计算输出以完成可靠行业任务的应用自然语言处理领域造成了严重问题。
URL
https://arxiv.org/abs/2601.15509
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Synthetic Data Augmentation for Multi-Task Chinese Porcelain Classification: A Stable Diffusion Approach
2026-01-21 09:14:22Ziyao Ling, Silvia Mirri, Paola Salomoni, Giovanni DelnevoAbstract
The scarcity of training data presents a fundamental challenge in applying deep learning to archaeological artifact classification, particularly for the rare types of Chinese porcelain. This study investigates whether synthetic images generated through Stable Diffusion with Low-Rank Adaptation (LoRA) can effectively augment limited real datasets for multi-task CNN-based porcelain classification. Using MobileNetV3 with transfer learning, we conducted controlled experiments comparing models trained on pure real data against those trained on mixed real-synthetic datasets (95:5 and 90:10 ratios) across four classification tasks: dynasty, glaze, kiln and type identification. Results demonstrate task-specific benefits: type classification showed the most substantial improvement (5.5\% F1-macro increase with 90:10 ratio), while dynasty and kiln tasks exhibited modest gains (3-4\%), suggesting that synthetic augmentation effectiveness depends on the alignment between generated features and task-relevant visual signatures. Our work contributes practical guidelines for deploying generative AI in archaeological research, demonstrating both the potential and limitations of synthetic data when archaeological authenticity must be balanced with data diversity.
Abstract (translated)
训练数据的稀缺性是将深度学习应用于考古文物分类，特别是对于稀有类型的中国瓷器时面临的基本挑战。本研究探讨了通过稳定扩散与低秩适应（LoRA）生成的合成图像是否可以有效扩充有限的真实数据集，以用于基于多任务CNN的瓷器分类。我们使用带有迁移学习的MobileNetV3进行了对比实验，将仅用真实数据训练的模型与混合有真实和合成数据集（95:5 和 90:10 比例）的模型在四个分类任务上进行比较：朝代、釉色、窑口及类型识别。结果显示特定任务的具体优势：类型分类表现出最显著的改进（使用90:10比例时，F1-macro分数提升了5.5%），而朝代和窑口任务则显示出较小的增长（3-4%）。这表明合成数据增强的效果取决于生成特征与任务相关的视觉签名之间的对齐程度。我们的研究为在考古学中部署生成式AI提供了实用指导，并展示了当需要平衡文物真实性与数据多样性时，合成数据的潜力及其局限性。
URL
https://arxiv.org/abs/2601.14791
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Transfer Learning from One Cancer to Another via Deep Learning Domain Adaptation
2026-01-21 05:50:34Justin Cheung, Samuel Savine, Calvin Nguyen, Lin Lu, Alhassan S. YasinAbstract
Supervised deep learning models often achieve excellent performance within their training distribution but struggle to generalize beyond it. In cancer histopathology, for example, a convolutional neural network (CNN) may classify cancer severity accurately for cancer types represented in its training data, yet fail on related but unseen types. Although adenocarcinomas from different organs share morphological features that might support limited cross-domain generalization, addressing domain shift directly is necessary for robust performance. Domain adaptation offers a way to transfer knowledge from labeled data in one cancer type to unlabeled data in another, helping mitigate the scarcity of annotated medical images. This work evaluates cross-domain classification performance among lung, colon, breast, and kidney adenocarcinomas. A ResNet50 trained on any single adenocarcinoma achieves over 98% accuracy on its own domain but shows minimal generalization to others. Ensembling multiple supervised models does not resolve this limitation. In contrast, converting the ResNet50 into a domain adversarial neural network (DANN) substantially improves performance on unlabeled target domains. A DANN trained on labeled breast and colon data and adapted to unlabeled lung data reaches 95.56% accuracy. We also examine the impact of stain normalization on domain adaptation. Its effects vary by target domain: for lung, accuracy drops from 95.56% to 66.60%, while for breast and colon targets, stain normalization boosts accuracy from 49.22% to 81.29% and from 78.48% to 83.36%, respectively. Finally, using Integrated Gradients reveals that DANNs consistently attribute importance to biologically meaningful regions such as densely packed nuclei, indicating that the model learns clinically relevant features and can apply them to unlabeled cancer types.
Abstract (translated)
监督深度学习模型通常在训练数据分布内表现出色，但在超出此范围时难以泛化。例如，在癌症病理学中，卷积神经网络（CNN）可以准确分类其训练数据包含的癌症类型的风险等级，但对相关的未见过的类型则可能失败。尽管来自不同器官的腺癌具有共享的形态特征，这些特征可能会支持有限的跨域泛化，但是直接解决领域偏移问题对于实现稳健性能是必要的。领域适应提供了一种方法，可以从一种癌症类型的标注数据中转移知识到另一种未标记的数据上，有助于缓解医学图像注释不足的问题。这项工作评估了肺、结肠、乳腺和肾脏腺癌之间的跨域分类表现。 使用单个腺癌训练的ResNet50在自身领域内达到超过98%的准确率，但在其他领域中的泛化能力非常有限。将多个监督模型进行集成并不能解决这一限制。相比之下，将ResNet50转换为领域对抗神经网络（DANN）显著提高了未标记目标领域的性能表现。一个使用标注好的乳腺和结肠数据训练并适应于未标记肺部数据的DANN达到了95.56%的准确率。 此外，这项研究还考察了染色标准化对域适应的影响。其效果因目标领域而异：对于肺腺癌，在应用染色标准化后，准确性从95.56%下降到66.60%，而对于乳腺和结肠的目标领域，染色标准化分别将准确率从49.22%提高到81.29%，以及从78.48%提升至83.36%。 最后，通过集成梯度方法揭示了DANN模型始终倾向于生物意义显著的区域（如密集核），表明该模型学习到了临床相关的特征，并能够将其应用于未标注的癌症类型。
URL
https://arxiv.org/abs/2601.14678
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OmniSpectra: A Unified Foundation Model for Native Resolution Astronomical Spectra
2026-01-21 04:39:32Md Khairul Islam, Judy FoxAbstract
We present OmniSpectra, the first native-resolution foundation model for astronomy spectra. Unlike traditional models, which are limited to fixed-length input sizes or configurations, OmniSpectra handles spectra of any length at their original size, without resampling or interpolation. Despite the large-scale spectroscopic data from diverse surveys fueling the rapid growth of astronomy, existing foundation models are limited to a fixed wavelength range and specific instruments. OmniSpectra is the first foundation model to learn simultaneously from multiple real-world spectra surveys with different configurations at a large scale. We achieve this by designing a novel architecture with adaptive patching across variable lengths, sinusoidal global wavelength encoding, local positional embeddings through depthwise convolution, and validity-aware self-attention masks. Allowing us to learn multi-scale spatial patterns while skipping attention for invalid patches. Even with a limited training example, OmniSpectra demonstrates excellent zero-shot generalization compared to methods tailored for specific tasks. This transfer learning capability makes this model the state-of-the-art across various astronomy tasks, including source classification, redshift estimation, and properties prediction for stars and galaxies. OmniSpectra reduces the need for training individual models for different tasks from scratch, establishing itself as the next-generation astronomy foundation model.
Abstract (translated)
我们介绍了OmniSpectra，这是首个用于天文学光谱的原生分辨率基础模型。与传统的模型不同，后者仅限于固定长度输入大小或配置，OmniSpectra能够处理任何长度的光谱，并且保持其原始尺寸，无需重采样或插值操作。尽管大规模的分光数据集来自各种不同的调查研究推动了天文学的快速发展，现有的基础模型仍然局限于固定的波长范围和特定仪器。OmniSpectra是首个同时从多个具有不同配置的真实世界光谱调查中进行大规模学习的基础模型。 我们通过设计一种新颖的架构来实现这一目标，该架构包括适应于可变长度的自适应补丁技术、全局波长编码（采用正弦函数）、通过深度卷积获取局部位置嵌入以及感知有效性的自我关注掩码。这使OmniSpectra能够学习多尺度的空间模式，并且可以跳过无效补丁的关注过程。 即便在训练示例有限的情况下，与专门针对特定任务的方法相比，OmniSpectra也表现出色的零样本泛化能力。这种迁移学习的能力使得该模型成为天文学各种任务中的最新技术，包括源分类、红移估计以及恒星和星系属性预测。OmniSpectra减少了从头开始为不同任务训练单独模型的需求，并确立了其作为下一代天文基础模型的地位。
URL
https://arxiv.org/abs/2601.15351
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Digital FAST: An AI-Driven Multimodal Framework for Rapid and Early Stroke Screening
2026-01-17 03:35:39Ngoc-Khai Hoang, Thi-Nhu-Mai Nguyen, Huy-Hieu PhamAbstract
Early identification of stroke symptoms is essential for enabling timely intervention and improving patient outcomes, particularly in prehospital settings. This study presents a fast, non-invasive multimodal deep learning framework for automatic binary stroke screening based on data collected during the F.A.S.T. assessment. The proposed approach integrates complementary information from facial expressions, speech signals, and upper-body movements to enhance diagnostic robustness. Facial dynamics are represented using landmark based features and modeled with a Transformer architecture to capture temporal dependencies. Speech signals are converted into mel spectrograms and processed using an Audio Spectrogram Transformer, while upper-body pose sequences are analyzed with an MLP-Mixer network to model spatiotemporal motion patterns. The extracted modality specific representations are combined through an attention-based fusion mechanism to effectively learn cross modal interactions. Experiments conducted on a self-collected dataset of 222 videos from 37 subjects demonstrate that the proposed multimodal model consistently outperforms unimodal baselines, achieving 95.83% accuracy and a 96.00% F1-score. The model attains a strong balance between sensitivity and specificity and successfully detects all stroke cases in the test set. These results highlight the potential of multimodal learning and transfer learning for early stroke screening, while emphasizing the need for larger, clinically representative datasets to support reliable real-world deployment.
Abstract (translated)
早期识别中风症状对于及时干预和改善患者预后至关重要，尤其是在院前环境中。本研究提出了一种基于F.A.S.T评估期间收集的数据的快速、非侵入性多模态深度学习框架，用于自动二元中风筛查。该方法整合了面部表情、语音信号及上半身动作之间的互补信息，以增强诊断稳健性。 面部动态通过关键点特征表示，并采用Transformer架构建模，捕捉时间依赖关系；语音信号被转换为梅尔频谱图并使用音频频谱图Transformer进行处理；上半身姿态序列则利用MLP-Mixer网络分析空间-时间运动模式。各模态特异性的表征是通过基于注意力的融合机制结合在一起，以有效学习跨模态交互。 在自行收集的数据集（包含37名受试者222段视频）上的实验表明，所提出的多模态模型始终优于单一模态基准模型，在准确性方面达到95.83%，F1得分高达96%。该模型实现了高敏感性和特异性之间的良好平衡，并成功检测了测试集中所有中风案例。 这些结果突显了多模态学习和迁移学习在早期中风筛查中的潜力，同时也强调需要更大的、具有临床代表性的数据集来支持可靠的现实世界部署。
URL
https://arxiv.org/abs/2601.11896
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Bio-inspired fine-tuning for selective transfer learning in image classification
2026-01-16 12:28:49Ana Davila, Jacinto Colan, Yasuhisa HasegawaAbstract
Deep learning has significantly advanced image analysis across diverse domains but often depends on large, annotated datasets for success. Transfer learning addresses this challenge by utilizing pre-trained models to tackle new tasks with limited labeled data. However, discrepancies between source and target domains can hinder effective transfer learning. We introduce BioTune, a novel adaptive fine-tuning technique utilizing evolutionary optimization. BioTune enhances transfer learning by optimally choosing which layers to freeze and adjusting learning rates for unfrozen layers. Through extensive evaluation on nine image classification datasets, spanning natural and specialized domains such as medical imaging, BioTune demonstrates superior accuracy and efficiency over state-of-the-art fine-tuning methods, including AutoRGN and LoRA, highlighting its adaptability to various data characteristics and distribution changes. Additionally, BioTune consistently achieves top performance across four different CNN architectures, underscoring its flexibility. Ablation studies provide valuable insights into the impact of BioTune's key components on overall performance. The source code is available at this https URL.
Abstract (translated)
深度学习在各种领域中显著推进了图像分析的发展，但通常依赖于大规模且标注完整的数据集才能取得成功。迁移学习通过利用预训练的模型来解决这个问题，在新任务上即使有少量标记数据也能发挥作用。然而，源域和目标域之间的差异会阻碍有效迁移学习的应用。 我们引入了一种新的自适应微调技术BioTune，它使用了进化优化方法。BioTune通过最优地选择要冻结哪些层以及调整未冻结层的学习率来增强迁移学习的效果。通过对九个图像分类数据集的广泛评估（包括自然和专门化领域如医学成像），BioTune展示了比当前最先进的微调方法（例如AutoRGN和LoRA）更高的准确性和效率，突显了它适应不同数据特性和分布变化的能力。此外，无论使用四种不同的CNN架构中的哪一种，BioTune都能保持顶级性能，这证明了它的灵活性。 消融研究为理解BioTune关键组件对整体性能的影响提供了宝贵的见解。源代码可在以下链接获得：[此链接]（请将方括号内的文本替换为您实际提供的GitHub或其他版本控制平台的URL）。
URL
https://arxiv.org/abs/2601.11235
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Unified Optimization of Source Weights and Transfer Quantities in Multi-Source Transfer Learning: An Asymptotic Framework
2026-01-15 18:46:54Qingyue Zhang, Chang Chu, Haohao Fu, Tianren Peng, Yanru Wu, Guanbo Huang, Yang Li, Shao-Lun HuangAbstract
Transfer learning plays a vital role in improving model performance in data-scarce scenarios. However, naive uniform transfer from multiple source tasks may result in negative transfer, highlighting the need to properly balance the contributions of heterogeneous sources. Moreover, existing transfer learning methods typically focus on optimizing either the source weights or the amount of transferred samples, while largely neglecting the joint consideration of the other. In this work, we propose a theoretical framework, Unified Optimization of Weights and Quantities (UOWQ), which formulates multi-source transfer learning as a parameter estimation problem grounded in an asymptotic analysis of a Kullback-Leibler divergence-based generalization error measure. The proposed framework jointly determines the optimal source weights and optimal transfer quantities for each source task. Firstly, we prove that using all available source samples is always optimal once the weights are properly adjusted, and we provide a theoretical explanation for this phenomenon. Moreover, to determine the optimal transfer weights, our analysis yields closed-form solutions in the single-source setting and develops a convex optimization-based numerical procedure for the multi-source case. Building on the theoretical results, we further propose practical algorithms for both multi-source transfer learning and multi-task learning settings. Extensive experiments on real-world benchmarks, including DomainNet and Office-Home, demonstrate that UOWQ consistently outperforms strong baselines. The results validate both the theoretical predictions and the practical effectiveness of our framework.
Abstract (translated)
迁移学习在数据稀缺场景中提升模型性能方面扮演着至关重要的角色。然而，直接从多个源任务进行均匀迁移可能会导致负向迁移（即迁移效果变差），这表明需要恰当地平衡异质来源的贡献。此外，现有的迁移学习方法通常专注于优化源权重或转移样本的数量，却很少兼顾两者的联合考虑。在这项工作中，我们提出了一种理论框架——权重量化统一优化（Unified Optimization of Weights and Quantities, UOWQ），该框架将多源迁移学习形式化为一个基于Kullback-Leibler散度泛化误差度量的渐近分析参数估计问题。所提出的框架可以同时确定每个源任务的最佳权重和最佳转移数量。 首先，我们证明了只要权重调整得当，使用所有可用的源样本总是最优的选择，并提供了对此现象的理论解释。此外，为了确定最佳迁移权重，我们的分析在单一来源设置下给出了封闭形式解，在多来源情况下开发了一种基于凸优化的数值方法。 在此基础上，我们进一步为多源迁移学习和多任务学习场景提出了实用算法。我们在包括DomainNet和Office-Home在内的真实世界基准数据集上进行了广泛实验，结果显示UOWQ始终优于强大的基线模型。这些结果验证了我们的框架理论预测的有效性和其实用性。
URL
https://arxiv.org/abs/2601.10779
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Decodable but not structured: linear probing enables Underwater Acoustic Target Recognition with pretrained audio embeddings
2026-01-13 09:15:31Hilde I. Hummel, Sandjai Bhulai, Rob D. van der Mei, Burooj GhaniAbstract
Increasing levels of anthropogenic noise from ships contribute significantly to underwater sound pollution, posing risks to marine ecosystems. This makes monitoring crucial to understand and quantify the impact of the ship radiated noise. Passive Acoustic Monitoring (PAM) systems are widely deployed for this purpose, generating years of underwater recordings across diverse soundscapes. Manual analysis of such large-scale data is impractical, motivating the need for automated approaches based on machine learning. Recent advances in automatic Underwater Acoustic Target Recognition (UATR) have largely relied on supervised learning, which is constrained by the scarcity of labeled data. Transfer Learning (TL) offers a promising alternative to mitigate this limitation. In this work, we conduct the first empirical comparative study of transfer learning for UATR, evaluating multiple pretrained audio models originating from diverse audio domains. The pretrained model weights are frozen, and the resulting embeddings are analyzed through classification, clustering, and similarity-based evaluations. The analysis shows that the geometrical structure of the embedding space is largely dominated by recording-specific characteristics. However, a simple linear probe can effectively suppress this recording-specific information and isolate ship-type features from these embeddings. As a result, linear probing enables effective automatic UATR using pretrained audio models at low computational cost, significantly reducing the need for a large amounts of high-quality labeled ship recordings.
Abstract (translated)
船舶产生的人为噪音水平上升，对水下声音污染的贡献显著增加，给海洋生态系统带来了风险。因此，监测船辐射噪声的影响变得至关重要。被动声学监测（PAM）系统被广泛用于这一目的，生成了跨越各种声音环境多年的大量水下录音数据。手动分析如此大规模的数据几乎是不可能的，这促使人们寻求基于机器学习的自动化方法的需求。最近，在自动水下声目标识别（UATR）方面取得的进展大多依赖于监督学习，而这种学习方式受限于标注数据的稀缺性。迁移学习（TL）为缓解这一限制提供了有希望的选择。 在本研究中，我们进行了首个关于转移学习用于UATR的经验比较分析，评估了来自不同音频领域的一系列预训练模型的效果。我们冻结了这些预训练模型的权重，并通过分类、聚类和基于相似性的评价方法来分析生成的嵌入（embeddings）。 分析结果显示，在嵌入空间中的几何结构主要由特定录音的特点所主导。然而，简单的线性探测可以有效抑制这一特点的信息并从这些嵌入中分离出船型特征。因此，通过使用预训练音频模型进行线性探测，能够以较低的计算成本实现有效的自动UATR，并显著减少了对高质量标注船舶录音的需求。 这种方法不仅提高了研究效率，还为解决数据标注问题提供了一种创新方案，促进了水下声学目标识别技术的发展。
URL
https://arxiv.org/abs/2601.08358
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VGG Induced Deep Hand Sign Language Detection
2026-01-13 06:39:29Subham Sharma, Sharmila SubudhiAbstract
Hand gesture recognition is an important aspect of human-computer interaction. It forms the basis of sign language for the visually impaired people. This work proposes a novel hand gesture recognizing system for the differently-abled persons. The model uses a convolutional neural network, known as VGG-16 net, for building a trained model on a widely used image dataset by employing Python and Keras libraries. Furthermore, the result is validated by the NUS dataset, consisting of 10 classes of hand gestures, fed to the model as the validation set. Afterwards, a testing dataset of 10 classes is built by employing Google's open source Application Programming Interface (API) that captures different gestures of human hand and the efficacy is then measured by carrying out experiments. The experimental results show that by combining a transfer learning mechanism together with the image data augmentation, the VGG-16 net produced around 98% accuracy.
Abstract (translated)
手部手势识别是人机交互的重要组成部分，它是视障人士使用的手语的基础。本研究提出了一种针对残障人士的手势识别系统。该模型采用卷积神经网络（称为VGG-16网络），利用Python和Keras库在广泛使用的一组图像数据上建立训练模型。此外，结果通过新加坡国立大学（NUS）数据集进行验证，该数据集中包含10类手部手势，并将其作为模型的验证集输入。随后，构建了一个包含10个类别测试数据的数据集，利用Google开源的应用编程接口（API），捕捉人类不同手势动作的有效性并执行实验以测量其效能。实验结果显示，通过结合迁移学习机制和图像数据增强技术，VGG-16网络达到了约98%的准确率。
URL
https://arxiv.org/abs/2601.08262
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