Transfer_Learning
-
Budget-Xfer: Budget-Constrained Source Language Selection for Cross-Lingual Transfer to African Languages
2026-03-29 11:55:45Tewodros Kederalah Idris, Roald Eiselen, Prasenjit MitraAbstract
Cross-lingual transfer learning enables NLP for low-resource languages by leveraging labeled data from higher-resource sources, yet existing comparisons of source language selection strategies do not control for total training data, confounding language selection effects with data quantity effects. We introduce Budget-Xfer, a framework that formulates multi-source cross-lingual transfer as a budget-constrained resource allocation problem. Given a fixed annotation budget B, our framework jointly optimizes which source languages to include and how much data to allocate from each. We evaluate four allocation strategies across named entity recognition and sentiment analysis for three African target languages (Hausa, Yoruba, Swahili) using two multilingual models, conducting 288 experiments. Our results show that (1) multi-source transfer significantly outperforms single-source transfer (Cohen's d = 0.80 to 1.98), driven by a structural budget underutilization bottleneck; (2) among multi-source strategies, differences are modest and non-significant; and (3) the value of embedding similarity as a selection proxy is task-dependent, with random selection outperforming similarity-based selection for NER but not sentiment analysis.
Abstract (translated)
跨语言迁移学习通过利用高资源语言的标注数据，使低资源语言的自然语言处理成为可能。然而，现有关于源语言选择策略的比较研究未控制总训练数据量，导致语言选择效应与数据量效应相互混淆。我们提出了Budget-Xfer框架，该框架将多源跨语言迁移形式化为预算约束下的资源分配问题。在固定标注预算B的条件下，我们的框架联合优化应纳入的源语言及从每种语言分配的数据量。我们使用两种多语言模型，在三个非洲目标语言（豪萨语、约鲁巴语、斯瓦希里语）的命名实体识别和情感分析任务上评估了四种分配策略，共开展288项实验。结果表明：（1）多源迁移显著优于单源迁移（Cohen's d = 0.80至1.98），其驱动力来自结构性预算利用不足的瓶颈；（2）在多源策略中，差异较小且不显著；（3）嵌入相似性作为选择代理的价值具有任务依赖性——在命名实体识别中随机选择优于基于相似性的选择，但在情感分析中并非如此。
URL
https://arxiv.org/abs/2603.27651
PDF
-
From Static to Dynamic: Exploring Self-supervised Image-to-Video Representation Transfer Learning
2026-03-27 16:56:50Yang Liu, Qianqian Xu, Peisong Wen, Siran Dai, Xilin Zhao, Qingming HuangAbstract
Recent studies have made notable progress in video representation learning by transferring image-pretrained models to video tasks, typically with complex temporal modules and video fine-tuning. However, fine-tuning heavy modules may compromise inter-video semantic separability, i.e., the essential ability to distinguish objects across videos. While reducing the tunable parameters hinders their intra-video temporal consistency, which is required for stable representations of the same object within a video. This dilemma indicates a potential trade-off between the intra-video temporal consistency and inter-video semantic separability during image-to-video transfer. To this end, we propose the Consistency-Separability Trade-off Transfer Learning (Co-Settle) framework, which applies a lightweight projection layer on top of the frozen image-pretrained encoder to adjust representation space with a temporal cycle consistency objective and a semantic separability constraint. We further provide a theoretical support showing that the optimized projection yields a better trade-off between the two properties under appropriate conditions. Experiments on eight image-pretrained models demonstrate consistent improvements across multiple levels of video tasks with only five epochs of self-supervised training. The code is available at this https URL.
Abstract (translated)
近期研究通过将图像预训练模型迁移至视频任务，在视频表示学习方面取得了显著进展，通常采用复杂的时间模块和视频微调。然而，微调重型模块可能会损害视频间语义可分性，即跨视频区分对象的基本能力。同时，减少可调参数会阻碍视频内时间一致性，而后者对于视频中同一对象的稳定表示至关重要。这一困境表明，在图像到视频迁移过程中，视频内时间一致性与视频间语义可分性之间存在潜在权衡。为此，我们提出了 Consistency-Separability Trade-off Transfer Learning (Co-Settle) 框架，该框架在冻结的图像预训练编码器之上应用轻量级投影层，通过时间循环一致性目标和语义可分性约束来调整表示空间。我们还提供了理论支持，证明在适当条件下，优化后的投影能在两种属性间取得更好的权衡。在八个图像预训练模型上的实验表明，仅通过五个周期的自监督训练，该方法在多层次视频任务上均实现了一致性提升。代码已公开于此https链接。
URL
https://arxiv.org/abs/2603.26597
PDF
-
Image Rotation Angle Estimation: Comparing Circular-Aware Methods
2026-03-26 11:57:28Maximilian WoehrerAbstract
Automatic image rotation estimation is a key preprocessing step in many vision pipelines. This task is challenging because angles have circular topology, creating boundary discontinuities that hinder standard regression methods. We present a comprehensive study of five circular-aware methods for global orientation estimation: direct angle regression with circular loss, classification via angular binning, unit-vector regression, phase-shifting coder, and circular Gaussian distribution. Using transfer learning from ImageNet-pretrained models, we systematically evaluate these methods across sixteen modern architectures by adapting their output heads for rotation-specific predictions. Our results show that probabilistic methods, particularly the circular Gaussian distribution, are the most robust across architectures, while classification achieves the best accuracy on well-matched backbones but suffers training instabilities on others. The best configuration (classification with EfficientViT-B3) achieves a mean absolute error (MAE) of 1.23° (mean across five independent runs) on the DRC-D dataset, while the circular Gaussian distribution with MambaOut Base achieves a virtually identical 1.24° with greater robustness across backbones. Training and evaluating our top-performing method-architecture combinations on COCO 2014, the best configuration reaches 3.71° MAE, improving substantially over prior work, with further improvement to 2.84° on the larger COCO 2017 dataset.
Abstract (translated)
URL
https://arxiv.org/abs/2603.25351
PDF
-
C2W-Tune: Cavity-to -Wall Transfer Learning for Thin Atrial Wall Segmentation in 3D Late Gadolinium-enhanced Magnetic Resonance
2026-03-26 03:35:35Yusri Al-Sanaani, Rebecca Thornhill, Sreeraman RajanAbstract
Accurate segmentation of the left atrial (LA) wall in 3D late gadolinium-enhanced MRI (LGE-MRI) is essential for wall thickness mapping and fibrosis quantification, yet it remains challenging due to the wall's thinness, complex anatomy, and low contrast. We propose C2W-Tune, a two-stage cavity-to-wall transfer framework that leverages a high-accuracy LA cavity model as an anatomical prior to improve thin-wall delineation. Using a 3D U-Net with a ResNeXt encoder and instance normalization, Stage 1 pre-trains the network to segment the LA cavity, learning robust atrial representations. Stage 2 transfers these weights and adapts the network to LA wall segmentation using a progressive layer-unfreezing schedule to preserve endocardial features while enabling wall-specific refinement. Experiments on the 2018 LA Segmentation Challenge dataset demonstrate substantial gains over an architecture-matched baseline trained from scratch: wall Dice improves from 0.623 to 0.814, and Surface Dice at 1 mm improves from 0.553 to 0.731. Boundary errors were substantially reduced, with the 95th-percentile Hausdorff distance (HD95) decreasing from 2.95 mm to 2.55 mm and the average symmetric surface distance (ASSD) from 0.71 mm to 0.63 mm. Furthermore, even with reduced supervision (70 training volumes sampled from the same training pool), C2W-Tune achieved a Dice score of 0.78 and an HD95 of 3.15 mm, maintaining competitive performance and exceeding multi-class benchmarks that typically report Dice values around 0.6-0.7. These results show that anatomically grounded task transfer with controlled fine-tuning improves boundary accuracy for thin LA wall segmentation in 3D LGE-MRI.
Abstract (translated)
在三维延迟钆增强磁共振成像（LGE-MRI）中，左心房（LA）壁的精确分割对于壁厚映射和纤维化定量至关重要，但由于心房壁薄、解剖结构复杂且图像对比度低，该任务仍具有挑战性。我们提出C2W-Tune，一种两阶段腔壁迁移框架，该框架利用高精度左心房腔体模型作为解剖先验信息以改善薄壁分割。第一阶段采用带有ResNeXt编码器和实例归一化的3D U-Net，预训练网络分割左心房腔体，学习稳健的心房表征。第二阶段迁移这些权重并调整网络以适应左心房壁分割，通过渐进式解冻策略在保留心内膜特征的同时实现壁特异性精细化。在2018年左心房分割挑战数据集上的实验表明，与从头训练的架构匹配基线相比，C2W-Tune取得显著提升：壁部Dice系数从0.623提高到0.814，1毫米表面Dice从0.553提高到0.731。边界误差大幅降低，95百分位Hausdorff距离（HD95）从2.95毫米降至2.55毫米，平均对称表面距离（ASSD）从0.71毫米降至0.63毫米。此外，即使在监督数据减少（从同一训练池中采样70个训练容积）的情况下，C2W-Tune仍达到0.78的Dice系数和3.15毫米的HD95，保持竞争力并超越通常报告Dice值在0.6-0.7之间的多类基准。这些结果表明，基于解剖学依据的任务迁移结合受控微调，可提升三维LGE-MRI中薄左心房壁分割的边界精度。
URL
https://arxiv.org/abs/2603.24992
PDF
-
The Gait Signature of Frailty: Transfer Learning based Deep Gait Models for Scalable Frailty Assessment
2026-03-25 15:44:37Laura McDaniel, Basudha Pal, Crystal Szczesny, Yuxiang Guo, Ryan Roemmich, Peter Abadir, Rama ChellappaAbstract
Frailty is a condition in aging medicine characterized by diminished physiological reserve and increased vulnerability to stressors. However, frailty assessment remains subjective, heterogeneous, and difficult to scale in clinical practice. Gait is a sensitive marker of biological aging, capturing multisystem decline before overt disability. Yet the application of modern computer vision to gait-based frailty assessment has been limited by small, imbalanced datasets and a lack of clinically representative benchmarks. In this work, we introduce a publicly available silhouette-based frailty gait dataset collected in a clinically realistic setting, spanning the full frailty spectrum and including older adults who use walking aids. Using this dataset, we evaluate how pretrained gait recognition models can be adapted for frailty classification under limited data conditions. We study both convolutional and hybrid attention-based architectures and show that predictive performance depends primarily on how pretrained representations are transferred rather than architectural complexity alone. Across models, selectively freezing low-level gait representations while allowing higher-level features to adapt yields more stable and generalizable performance than either full fine-tuning or rigid freezing. Conservative handling of class imbalance further improves training stability, and combining complementary learning objectives enhances discrimination between clinically adjacent frailty states. Interpretability analyses reveal consistent model attention to lower-limb and pelvic regions, aligning with established biomechanical correlates of frailty. Together, these findings establish gait-based representation learning as a scalable, non-invasive, and interpretable framework for frailty assessment and support the integration of modern biometric modeling approaches into aging research and clinical practice.
Abstract (translated)
衰弱症是老年医学中的一种状态，其特征是生理储备下降和对压力源的易感性增加。然而，在临床实践中，衰弱评估仍具有主观性、异质性且难以规模化。步态是生物衰老的敏感标志，能在明显失能前捕捉多系统衰退。但现代计算机视觉在基于步态的衰弱评估中的应用，一直受限于数据集规模小、类别不平衡以及缺乏临床代表性基准。在本研究中，我们发布了一个在临床真实场景下收集的、基于轮廓的衰弱步态公开数据集，该数据集覆盖了完整的衰弱谱系，并包含了使用助行器的老年人。利用此数据集，我们评估了预训练步态识别模型如何在数据有限条件下适配于衰弱分类任务。我们研究了卷积架构和混合注意力架构，并证明预测性能主要取决于预训练表征的迁移方式，而非单纯依赖架构复杂度。跨模型分析表明，与全参数微调或僵化冻结相比，选择性冻结低层步态表征同时允许高层特征自适应，能获得更稳定且泛化性更好的性能。对类别不平衡的保守处理进一步提升了训练稳定性，而结合互补学习目标则增强了对临床相邻衰弱状态的判别能力。可解释性分析显示，模型持续关注下肢和骨盆区域，这与已确立的衰弱生物力学相关指标一致。综上，这些发现确立了基于步态的表征学习作为一种可规模化、非侵入且可解释的衰弱评估框架，并支持将现代生物特征建模方法整合到衰老研究和临床实践中。
URL
https://arxiv.org/abs/2603.24434
PDF
-
Causal Transfer in Medical Image Analysis
2026-03-25 15:04:53Mohammed M. Abdelsamea, Daniel Tweneboah Anyimadu, Tasneem Selim, Saif Alzubi, Lei Zhang, Ahmed Karam Eldaly, Xujiong YeAbstract
Medical imaging models frequently fail when deployed across hospitals, scanners, populations, or imaging protocols due to domain shift, limiting their clinical reliability. While transfer learning and domain adaptation address such shifts statistically, they often rely on spurious correlations that break under changing conditions. On the other hand, causal inference provides a principled way to identify invariant mechanisms that remain stable across environments. This survey introduces and systematises Causal Transfer Learning (CTL) for medical image analysis. This paradigm integrates causal reasoning with cross-domain representation learning to enable robust and generalisable clinical AI. We frame domain shift as a causal problem and analyse how structural causal models, invariant risk minimisation, and counterfactual reasoning can be embedded within transfer learning pipelines. We studied spanning classification, segmentation, reconstruction, anomaly detection, and multimodal imaging, and organised them by task, shift type, and causal assumption. A unified taxonomy is proposed that connects causal frameworks and transfer mechanisms. We further summarise datasets, benchmarks, and empirical gains, highlighting when and why causal transfer outperforms correlation-based domain adaptation. Finally, we discuss how CTL supports fairness, robustness, and trustworthy deployment in multi-institutional and federated settings, and outline open challenges and research directions for clinically reliable medical imaging AI.
Abstract (translated)
医学影像模型在跨医院、扫描仪、人群或成像协议部署时，常因域偏移而失效，限制了其临床可靠性。尽管迁移学习和域适应从统计角度缓解此类偏移，但它们往往依赖在条件变化时易失效的虚假关联。另一方面，因果推理提供了一种识别跨环境保持稳定的不变机制的规范方法。本综述针对医学影像分析提出并系统化了因果迁移学习（CTL）范式。该范式将因果推理与跨域表示学习相结合，以实现鲁棒且可泛化的临床人工智能。我们将域偏移构建为因果问题，并分析了如何将结构因果模型、不变风险最小化和反事实推理嵌入迁移学习流程。我们研究了分类、分割、重建、异常检测和多模态成像等任务，按任务类型、偏移类别和因果假设进行组织。提出了统一分类法，连接因果框架与迁移机制。进一步总结了数据集、基准和实证增益，强调了因果迁移在何时及为何优于基于关联的域适应。最后讨论了CTL如何支持多机构及联邦学习场景下的公平性、鲁棒性和可信部署，并概述了实现临床可靠医学影像人工智能的开放挑战与研究方向。
URL
https://arxiv.org/abs/2603.24388
PDF
-
Retinal Disease Classification from Fundus Images using CNN Transfer Learning
2026-03-24 23:40:48Ali AkramAbstract
Retinal diseases remain among the leading preventable causes of visual impairment worldwide. Automated screening based on fundus image analysis has the potential to expand access to early detection, particularly in underserved populations. This paper presents a reproducible deep learning pipeline for binary retinal disease risk classification from publicly available fundus photographs. We implement and compare a baseline convolutional neural network with a transfer learning approach using a pretrained VGG16 backbone and evaluate generalization on held-out data. To address class imbalance, we apply class weighting and report standard classification metrics including accuracy, precision, recall, F1-score, confusion matrices, and ROC-AUC. The VGG16 transfer learning model achieves 90.8% test accuracy with a weighted F1-score of 0.90, substantially outperforming the baseline CNN (83.1% accuracy). Results indicate that transfer learning improves discrimination compared to a baseline CNN, while also revealing remaining challenges in sensitivity to minority disease cases. We discuss practical limitations related to dataset characteristics, class imbalance, and threshold selection, and provide guidance for reproducibility and future improvements for clinically reliable screening
Abstract (translated)
视网膜疾病仍是全球可预防性视觉障碍的主要病因之一。基于眼底图像分析的自动化筛查有潜力扩大早期检测的可及性，尤其对医疗资源匮乏地区。本研究提出一个可复现的深度学习流程，用于基于公开眼底照片的视网膜疾病风险二分类。我们实现并比较了基线卷积神经网络与采用预训练VGG16骨干网络的迁移学习方法，并在保留测试集上评估泛化能力。为处理类别不平衡，我们应用类别加权策略，并报告准确率、精确率、召回率、F1分数、混淆矩阵及ROC-AUC等标准分类指标。VGG16迁移学习模型在测试集上达到90.8%的准确率与0.90的加权F1分数，显著优于基线CNN（83.1%准确率）。结果表明迁移学习相较于基线CNN提升了判别能力，同时揭示了对少数疾病病例敏感度仍存在的挑战。我们讨论了与数据集特征、类别不平衡及阈值选择相关的实践局限，并为实现临床可靠筛查的可复现性与未来改进提供指导。
URL
https://arxiv.org/abs/2603.23785
PDF
-
Probabilistic Geometric Alignment via Bayesian Latent Transport for Domain-Adaptive Foundation Models
2026-03-24 23:35:08Kuepon Aueawatthanaphisut, Kuepon AueawatthanaphisutAbstract
Adapting large-scale foundation models to new domains with limited supervision remains a fundamental challenge due to latent distribution mismatch, unstable optimization dynamics, and miscalibrated uncertainty propagation. This paper introduces an uncertainty-aware probabilistic latent transport framework that formulates domain adaptation as a stochastic geometric alignment problem in representation space. A Bayesian transport operator is proposed to redistribute latent probability mass along Wasserstein-type geodesic trajectories, while a PAC-Bayesian regularization mechanism constrains posterior model complexity to mitigate catastrophic overfitting. The proposed formulation yields theoretical guarantees on convergence stability, loss landscape smoothness, and sample efficiency under distributional shift. Empirical analyses demonstrate substantial reduction in latent manifold discrepancy, accelerated transport energy decay, and improved covariance calibration compared with deterministic fine-tuning and adversarial domain adaptation baselines. Furthermore, bounded posterior uncertainty evolution indicates enhanced probabilistic reliability during cross-domain transfer. By establishing a principled connection between stochastic optimal transport geometry and statistical generalization theory, the proposed framework provides new insights into robust adaptation of modern foundation architectures operating in heterogeneous environments. These findings suggest that uncertainty-aware probabilistic alignment constitutes a promising paradigm for reliable transfer learning in next-generation deep representation systems.
Abstract (translated)
URL
https://arxiv.org/abs/2603.23783
PDF
-
Detection and Classification of Cancerous Cells in Pap Smears: An Ensemble Strategy for the RIVA Cervical Cytology Challenge
2026-03-24 22:03:14Lautaro Kogan, Mar\'ia Victoria R\'iosAbstract
Automated detection and classification of cervical cells in conventional Pap smear images can strengthen cervical cancer screening at scale by reducing manual workload, improving triage, and increasing consistency across readers. However, it is challenged by severe class imbalance and frequent nuclear overlap. We present our approach to the RIVA Cervical Cytology Challenge (ISBI 2026), which requires multi-class detection of eight Bethesda cell categories under these conditions. Using YOLOv11m as the base architecture, we systematically evaluate three strategies to improve detection performance: loss reweighting, data resampling and transfer learning. We build an ensemble by combining models trained under each strategy, promoting complementary detection behavior and combining them through Weighted Boxes Fusion (WBF). The ensemble achieves a mAP50-95 of 0.201 on the preliminary test set and 0.147 on the final test set, representing a 29% improvement over the best individual model on the final test set and demonstrating the effectiveness of combining complementary imbalance mitigation strategies.
Abstract (translated)
基于传统巴氏涂片图像的宫颈细胞自动检测与分类可通过减少人工工作量、改善分流诊断并提升阅片一致性，从而加强大规模宫颈癌筛查。然而，该方法面临严重的类别不平衡和频繁的细胞核重叠挑战。本文介绍了我们针对RIVA宫颈细胞学挑战赛（ISBI 2026）的研究方案，该赛事要求在所述条件下对八类贝塞斯达细胞分类进行多类检测。以YOLOv11m为基础架构，我们系统评估了三种提升检测性能的策略：损失函数重加权、数据重采样与迁移学习。通过整合各策略训练的模型构建集成模型，促进互补检测行为，并采用加权框融合（WBF）进行结果合并。该集成模型在初步测试集上达到0.201的mAP50-95，在最终测试集上达到0.147，较最终测试集上的最佳单一模型提升29%，证明了结合互补性不平衡缓解策略的有效性。
URL
https://arxiv.org/abs/2603.23742
PDF
-
Beyond Hate: Differentiating Uncivil and Intolerant Speech in Multimodal Content Moderation
2026-03-24 09:22:36Nils A. Herrmann, Tobias Eder, Jingyi He, Georg GrohAbstract
Current multimodal toxicity benchmarks typically use a single binary hatefulness label. This coarse approach conflates two fundamentally different characteristics of expression: tone and content. Drawing on communication science theory, we introduce a fine-grained annotation scheme that distinguishes two separable dimensions: incivility (rude or dismissive tone) and intolerance (content that attacks pluralism and targets groups or identities) and apply it to 2,030 memes from the Hateful Memes dataset. We evaluate different vision-language models under coarse-label training, transfer learning across label schemes and a joint learning approach that combines the coarse hatefulness label with our fine-grained annotations. Our results show that fine-grained annotations complement existing coarse labels and, when used jointly, improve overall model performance. Moreover, models trained with the fine-grained scheme exhibit more balanced moderation-relevant error profiles and are less prone to under-detection of harmful content than models trained on hatefulness labels alone (FNR-FPR, the difference between false negative and false positive rates: 0.74 to 0.42 for LLaVA-1.6-Mistral-7B; 0.54 to 0.28 for Qwen2.5-VL-7B). This work contributes to data-centric approaches in content moderation by improving the reliability and accuracy of moderation systems through enhanced data quality. Overall, combining both coarse and fine-grained labels provides a practical route to more reliable multimodal moderation.
Abstract (translated)
当前多模态毒性基准通常采用单一的二元仇恨标签。这种粗略方法混淆了表达的两个根本不同特征：语气与内容。基于传播学理论，我们引入了一种细粒度标注方案，区分了两个可分离的维度：不文明（粗鲁或轻蔑的语气）与不宽容（攻击多元主义并针对群体或身份的内容），并将其应用于仇恨模因数据集中的2,030个模因。我们在粗标签训练、跨标签方案的迁移学习以及结合粗仇恨标签与我们细粒度标注的联合学习方法下，评估了不同的视觉-语言模型。结果表明，细粒度标注补充了现有的粗标签，联合使用时能提升模型整体性能。此外，使用细粒度方案训练的模型表现出更平衡的审核相关误差特征，且相比仅使用仇恨标签训练的模型，更不易漏检有害内容（假阴性率与假阳性率之差：LLaVA-1.6-Mistral-7B从0.74降至0.42；Qwen2.5-VL-7B从0.54降至0.28）。本研究通过提升数据质量来增强审核系统的可靠性与准确性，为内容审核中以数据为中心的方法做出了贡献。总体而言，结合粗标签与细粒度标签为实现更可靠的多模态审核提供了一条实用路径。
URL
https://arxiv.org/abs/2603.22985
PDF
-
Restoring Neural Network Plasticity for Faster Transfer Learning
2026-03-21 15:41:58Xander Coetzer, Arn\'e Schreuder, Anna Sergeevna BosmanAbstract
Transfer learning with models pretrained on ImageNet has become a standard practice in computer vision. Transfer learning refers to fine-tuning pretrained weights of a neural network on a downstream task, typically unrelated to ImageNet. However, pretrained weights can become saturated and may yield insignificant gradients, failing to adapt to the downstream task. This hinders the ability of the model to train effectively, and is commonly referred to as loss of neural plasticity. Loss of plasticity may prevent the model from fully adapting to the target domain, especially when the downstream dataset is atypical in nature. While this issue has been widely explored in continual learning, it remains relatively understudied in the context of transfer learning. In this work, we propose the use of a targeted weight re-initialization strategy to restore neural plasticity prior to fine-tuning. Our experiments show that both convolutional neural networks (CNNs) and vision transformers (ViTs) benefit from this approach, yielding higher test accuracy with faster convergence on several image classification benchmarks. Our method introduces negligible computational overhead and is compatible with common transfer learning pipelines.
Abstract (translated)
在ImageNet上预训练的模型进行迁移学习已成为计算机视觉领域的标准做法。迁移学习指的是在后续任务（通常与ImageNet无关）上微调神经网络的预训练权重。然而，预训练权重可能趋于饱和，产生不显著的梯度变化，从而无法适应下游任务。这阻碍了模型的有效训练，通常被称为神经可塑性丧失。神经可塑性丧失可能使模型无法充分适应目标领域，尤其当后续数据集本身具有非典型特征时。尽管该问题在持续学习领域已被广泛研究，但在迁移学习语境中仍相对缺乏探索。本研究提出采用针对性权重重初始化策略，在微调前恢复神经可塑性。实验表明，卷积神经网络（CNNs）和视觉Transformer（ViTs）均能从此方法中受益，在多个图像分类基准数据集上实现更高的测试准确率与更快的收敛速度。该方法几乎不增加计算开销，且与常见迁移学习流程兼容。
URL
https://arxiv.org/abs/2603.20860
PDF
-
Leveraging Natural Language Processing and Machine Learning for Evidence-Based Food Security Policy Decision-Making in Data-Scarce Making
2026-03-20 18:54:09Karan Kumar Singh, Nikita GajbhiyeAbstract
Food security policy formulation in data-scarce regions remains a critical challenge due to limited structured datasets, fragmented textual reports, and demographic bias in decision-making systems. This study proposes ZeroHungerAI, an integrated Natural Language Processing (NLP) and Machine Learning (ML) framework designed for evidence-based food security policy modeling under extreme data scarcity. The system combines structured socio-economic indicators with contextual policy text embeddings using a transfer learning based DistilBERT architecture. Experimental evaluation on a 1200-sample hybrid dataset across 25 districts demonstrates superior predictive performance, achieving 91 percent classification accuracy, 0.89 precision, 0.85 recall, and an F1 score of 0.86 under imbalanced conditions. Comparative analysis shows a 13 percent performance improvement over classical SVM and 17 percent over Logistic Regression models. Precision Recall evaluation confirms robust minority class detection (average precision around 0.88). Fairness aware optimization reduces demographic parity difference to 3 percent, ensuring equitable rural urban policy inference. The results validate that transformer based contextual learning significantly enhances policy intelligence in low resource governance environments, enabling scalable and bias aware hunger prediction systems.
Abstract (translated)
在数据稀缺地区制定粮食安全政策仍是一项关键挑战，原因在于结构化数据集有限、文本报告碎片化以及决策系统中存在人口统计偏差。本研究提出ZeroHungerAI——一个集成自然语言处理（NLP）与机器学习（ML）的框架，专为极端数据稀缺条件下的循证粮食安全政策建模而设计。该系统通过基于迁移学习的DistilBERT架构，将结构化社会经济指标与情境化政策文本嵌入相结合。在涵盖25个地区、共1200个样本的混合数据集上的实验评估表明，该框架在不平衡条件下展现出卓越的预测性能，分类准确率达91%，精确率为0.89，召回率为0.85，F1分数为0.86。对比分析显示，其性能较经典支持向量机（SVM）提升13%，较逻辑回归模型提升17%。精确率-召回率评估证实了其对少数类样本的稳健检测能力（平均精确率约0.88）。通过公平性感知优化，人口统计均等差异降至3%，确保了城乡政策推论的公平性。研究结果验证了基于Transformer的情境化学习能显著提升资源匮乏治理环境中的政策智能，为实现可扩展且具备偏差意识的饥饿预测系统提供了可能。
URL
https://arxiv.org/abs/2603.20425
PDF
-
Fine-tuning Timeseries Predictors Using Reinforcement Learning
2026-03-20 15:44:40Hugo Cazaux, Ralph Rudd, Hlynur Stef\'ansson, Sverrir \'Olafsson, Eyj\'olfur Ingi \'AsgeirssonAbstract
This chapter presents three major reinforcement learning algorithms used for fine-tuning financial forecasters. We propose a clear implementation plan for backpropagating the loss of a reinforcement learning task to a model trained using supervised learning, and compare the performance before and after the fine-tuning. We find an increase in performance after fine-tuning, and transfer learning properties to the models, indicating the benefits of fine-tuning. We also highlight the tuning process and empirical results for future implementation by practitioners.
Abstract (translated)
本章介绍了三种用于微调金融预测器的主要强化学习算法。我们提出了将强化学习任务的损失反向传播至经监督学习训练的模型的清晰实施方案，并比较了微调前后的性能表现。研究发现微调后性能有所提升，且模型展现出迁移学习特性，这表明微调具有显著效益。同时，我们强调了调优过程及实证结果，以供实践者未来实施参考。
URL
https://arxiv.org/abs/2603.20063
PDF
-
A Multi-Modal CNN-LSTM Framework with Multi-Head Attention and Focal Loss for Real-Time Elderly Fall Detection
2026-03-20 03:04:24Lijie Zhou, Luran WangAbstract
The increasing global aging population has intensified the demand for reliable health monitoring systems, particularly those capable of detecting critical events such as falls among elderly individuals. Traditional fall detection approaches relying on single-modality acceleration data suffer from high false alarm rates, while conventional machine learning methods require extensive hand-crafted feature engineering. This paper proposes a novel multi-modal deep learning framework, MultiModalFallDetector, designed for real-time elderly fall detection using wearable sensors. Our approach integrates multiple innovations: a multi-scale CNN-based feature extractor capturing motion dynamics at varying temporal resolutions; fusion of tri-axial accelerometer, gyroscope, and four-channel physiological signals; incorporation of a multi-head self-attention mechanism for dynamic temporal weighting; adoption of Focal Loss to mitigate severe class imbalance; introduction of an auxiliary activity classification task for regularization; and implementation of transfer learning from UCI HAR to SisFall dataset. Extensive experiments on the SisFall dataset, which includes real-world simulated fall trials from elderly participants (aged 60-85), demonstrate that our framework achieves an F1-score of 98. 7, Recall of 98. 9, and AUC-ROC of 99. 4, significantly outperforming baseline methods including traditional machine learning and standard deep learning approaches. The model maintains sub- 50ms inference latency on edge devices, confirming its suitability for real-time deployment in geriatric care settings.
Abstract (translated)
全球老龄化人口的增长加剧了对可靠健康监测系统的需求，尤其是能够检测老年人跌倒等关键事件的系统。传统依赖单一模态加速度数据的跌倒检测方法误报率较高，而常规机器学习方法则需要大量手工特征工程。本研究提出一种新颖的多模态深度学习框架MultiModalFallDetector，专为使用可穿戴传感器进行实时老年人跌倒检测而设计。该方法整合了多项创新：基于多尺度CNN的特征提取器，可捕获不同时间分辨率的运动动态；融合三轴加速度计、陀螺仪和四通道生理信号；引入多头自注意力机制实现动态时间加权；采用Focal Loss缓解严重的类别不平衡问题；引入辅助活动分类任务进行正则化；并实施从UCI HAR数据集到SisFall数据集的迁移学习。在包含60-85岁老年参与者真实模拟跌倒试验的SisFall数据集上进行的大量实验表明，本框架的F1分数达98.7，召回率达98.9，AUC-ROC值达99.4，显著优于包括传统机器学习和标准深度学习方法在内的基线方案。该模型在边缘设备上保持低于50毫秒的推理延迟，证实其适用于老年护理场景的实时部署。
URL
https://arxiv.org/abs/2603.22313
PDF
-
Interpretable Prostate Cancer Detection using a Small Cohort of MRI Images
2026-03-19 03:40:07Vahid Monfared, Mohammad Hadi Gharib, Ali Sabri, Maryam Shahali, Farid Rashidi, Amit Mehta, Reza RawassizadehAbstract
Prostate cancer is a leading cause of mortality in men, yet interpretation of T2-weighted prostate MRI remains challenging due to subtle and heterogeneous lesions. We developed an interpretable framework for automatic cancer detection using a small dataset of 162 T2-weighted images (102 cancer, 60 normal), addressing data scarcity through transfer learning and augmentation. We performed a comprehensive comparison of Vision Transformers (ViT, Swin), CNNs (ResNet18), and classical methods (Logistic Regression, SVM, HOG+SVM). Transfer-learned ResNet18 achieved the best performance (90.9% accuracy, 95.2% sensitivity, AUC 0.905) with only 11M parameters, while Vision Transformers showed lower performance despite substantially higher complexity. Notably, HOG+SVM achieved comparable accuracy (AUC 0.917), highlighting the effectiveness of handcrafted features in small datasets. Unlike state-of-the-art approaches relying on biparametric MRI (T2+DWI) and large cohorts, our method achieves competitive performance using only T2-weighted images, reducing acquisition complexity and computational cost. In a reader study of 22 cases, five radiologists achieved a mean sensitivity of 67.5% (Fleiss Kappa = 0.524), compared to 95.2% for the AI model, suggesting potential for AI-assisted screening to reduce missed cancers and improve consistency. Code and data are publicly available.
Abstract (translated)
URL
https://arxiv.org/abs/2603.18460
PDF
-
EDM-ARS: A Domain-Specific Multi-Agent System for Automated Educational Data Mining Research
2026-03-18 20:45:45Chenguang Pan, Zhou Zhang, Weixuan Xiao, Chengyuan YaoAbstract
In this technical report, we present the Educational Data Mining Automated Research System (EDM-ARS), a domain-specific multi-agent pipeline that automates end-to-end educational data mining (EDM) research. We conceptualize EDM-ARS as a general framework for domain-aware automated research pipelines, where educational expertise is embedded into each stage of the research lifecycle. As a first instantiation of this framework, we focus on predictive modeling tasks. Within this scope, EDM-ARS orchestrates five specialized LLM-powered agents (ProblemFormulator, DataEngineer, Analyst, Critic, and Writer) through a state-machine coordinator that supports revision loops, checkpoint-based recovery, and sandboxed code execution. Given a research prompt and a dataset, EDM-ARS produces a complete LaTeX manuscript with real Semantic Scholar citations, validated machine learning analyses, and automated methodological peer review. We also provide a detailed description of the system architecture, the three-tier data registry design that encodes educational domain expertise, the specification of each agent, the inter-agent communication protocol, and mechanisms for error-handling and self-correction. Finally, we discuss current limitations, including single-dataset scope and formulaic paper output, and outline a phased roadmap toward causal inference, transfer learning, psychometric, and multi-dataset generalization. EDM-ARS is released as an open-source project to support the educational research community.
Abstract (translated)
URL
https://arxiv.org/abs/2603.18273
PDF
-
DeepCORO-CLIP: A Multi-View Foundation Model for Comprehensive Coronary Angiography Video-Text Analysis and External Validation
2026-03-18 12:54:24Sarra Harrabi, Yichen Wu, Geoffrey H. Tison, Minhaj Ansari, Milos Vukadinovic, David Ouyang, Joshua P. Barrios, Jacques Delfrate, Robert AvramAbstract
Coronary angiography is the reference standard for evaluating coronary artery disease, yet visual interpretation remains variable between readers. Existing artificial intelligence methods typically analyze single frames or projections and focus mainly on stenosis, limiting comprehensive coronary assessment. We present DeepCORO-CLIP, a multi-view foundation model trained with video-text contrastive learning on 203,808 angiography videos from 28,117 patients across 32,473 studies at the Montreal Heart Institute and externally validated on 4,249 studies from the University of California, San Francisco. DeepCORO-CLIP integrates multiple projections with attention-based pooling for study-level assessment across diagnostic, prognostic, and disease progression tasks. For significant stenosis detection, the model achieved an AUROC of 0.888 internally and 0.89 on external validation. Mean absolute error against core laboratory quantitative coronary angiography was 13.6%, lower than clinical reports at 19.0%. The model also performed strongly for chronic total occlusion, intracoronary thrombus, and coronary calcification detection. Transfer learning enabled prediction of one-year major adverse cardiovascular events with AUROC 0.79 and estimation of left ventricular ejection fraction with mean absolute error 7.3%. Embeddings also captured disease progression across serial examinations. With a mean inference time of 4.2 seconds in hospital deployment, DeepCORO-CLIP provides a foundation for automated coronary angiography interpretation at the point of care. Code, sample data, model weights, and deployment infrastructure are publicly released.
Abstract (translated)
URL
https://arxiv.org/abs/2603.17675
PDF
-
A scalable neural bundle map for multiphysics prediction in lithium-ion battery across varying configurations
2026-03-17 23:15:34Zhiwei Zhao, Changqing Liu, Jie Lin, Fan Yang, Yifan Zhang, Yan Jin, Yingguang LiAbstract
Efficient and accurate prediction of Multiphysics evolution across diverse cell geometries is fundamental to the design, management and safety of lithium-ion batteries. However, existing computational frameworks struggle to capture the coupled electrochemical, thermal, and mechanical dynamics across diverse cell geometries and varying operating conditions. Here, we present a Neural Bundle Map (NBM), a mathematically rigorous framework that reformulates multiphysics evolution as a bundle map over a geometric base manifold. This approach enables the complete decoupling of geometric complexity from underlying physical laws, ensuring strong operator continuity across varying domains. Our framework achieves high-fidelity spatiotemporal predictions with a normalized mean absolute error of less than 1% across varying configurations, while maintaining stability during long-horizon forecasting far beyond the training window and reducing computational costs by two orders of magnitude compared with conventional solvers. Leveraging this capability, we rapidly explored a vast configurational space to identify an optimal battery design that yields a 38% increase in energy density while adhering to thermal safety constraints. Furthermore, the NBM demonstrates remarkable scalability to multi-cell systems through few-shot transfer learning, providing a foundational paradigm for the intelligent design and real-time monitoring of complex energy storage infrastructures.
Abstract (translated)
URL
https://arxiv.org/abs/2603.17209
PDF
-
Out-of-Distribution Object Detection in Street Scenes via Synthetic Outlier Exposure and Transfer Learning
2026-03-17 05:06:28Sadia Ilyas, Annika M\"utze, Klaus Friedrichs, Thomas Kurbiel, Matthias RottmannAbstract
Out-of-distribution (OOD) object detection is an important yet underexplored task. A reliable object detector should be able to handle OOD objects by localizing and correctly classifying them as OOD. However, a critical issue arises when such atypical objects are completely missed by the object detector and incorrectly treated as background. Existing OOD detection approaches in object detection often rely on complex architectures or auxiliary branches and typically do not provide a framework that treats in-distribution (ID) and OOD in a unified way. In this work, we address these limitations by enabling a single detector to detect OOD objects, that are otherwise silently overlooked, alongside ID objects. We present \textbf{SynOE-OD}, a \textbf{Syn}thetic \textbf{O}utlier-\textbf{E}xposure-based \textbf{O}bject \textbf{D}etection framework, that leverages strong generative models, like Stable Diffusion, and Open-Vocabulary Object Detectors (OVODs) to generate semantically meaningful, object-level data that serve as outliers during training. The generated data is used for transfer-learning to establish strong ID task performance and supplement detection models with OOD object detection robustness. Our approach achieves state-of-the-art average precision on an established OOD object detection benchmark, where OVODs, such as GroundingDINO, show limited zero-shot performance in detecting OOD objects in street-scenes.
Abstract (translated)
URL
https://arxiv.org/abs/2603.16122
PDF
-
Evolutionary Transfer Learning for Dragonchess
2026-03-16 13:58:16Jim O'Connor, Annika Hoag, Sarah Goyette, Gary B. ParkerAbstract
Dragonchess, a three-dimensional chess variant introduced by Gary Gygax, presents unique strategic and computational challenges that make it an ideal environment for studying the transfer of artificial intelligence (AI) heuristics across domains. In this work, we introduce Dragonchess as a novel testbed for AI research and provide an open-source, Python-based game engine for community use. Our research investigates evolutionary transfer learning by adapting heuristic evaluation functions directly from Stockfish, a leading chess engine, and subsequently optimizing them using Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Initial trials showed that direct heuristic transfers were inadequate due to Dragonchess's distinct multi-layer structure and movement rules. However, evolutionary optimization significantly improved AI agent performance, resulting in superior gameplay demonstrated through empirical evaluation in a 50-round Swiss-style tournament. This research establishes the effectiveness of evolutionary methods in adapting heuristic knowledge to structurally complex, previously unexplored game domains.
Abstract (translated)
URL
https://arxiv.org/abs/2603.15297
PDF
