Transfer_Learning
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HATL: Hierarchical Adaptive-Transfer Learning Framework for Sign Language Machine Translation
2026-02-26 17:22:42Nada Shahin, Leila IsmailAbstract
Sign Language Machine Translation (SLMT) aims to bridge communication between Deaf and hearing individuals. However, its progress is constrained by scarce datasets, limited signer diversity, and large domain gaps between sign motion patterns and pretrained representations. Existing transfer learning approaches in SLMT are static and often lead to overfitting. These challenges call for the development of an adaptive framework that preserves pretrained structure while remaining robust across linguistic and signing variations. To fill this void, we propose a Hierarchical Adaptive Transfer Learning (HATL) framework, where pretrained layers are progressively and dynamically unfrozen based on training performance behavior. HATL combines dynamic unfreezing, layer-wise learning rate decay, and stability mechanisms to preserve generic representations while adapting to sign characteristics. We evaluate HATL on Sign2Text and Sign2Gloss2Text translation tasks using a pretrained ST-GCN++ backbone for feature extraction and the Transformer and an adaptive transformer (ADAT)for translation. To ensure robust multilingual generalization, we evaluate the proposed approach across three datasets: RWTH-PHOENIXWeather-2014 (PHOENIX14T), Isharah, and MedASL. Experimental results show that HATL consistently outperforms traditional transfer learning approaches across tasks and models, with ADAT achieving BLEU-4 improvements of 15.0% on PHOENIX14T and Isharah and 37.6% on MedASL.
Abstract (translated)
手语机器翻译旨在促进听障人士与健听人士之间的沟通。然而，其发展受限于数据集稀缺、签署者多样性不足，以及手语动作模式与预训练表示之间存在较大领域差异。现有SLMT中的迁移学习方法通常是静态的，容易导致过拟合。这些挑战要求开发一种自适应框架，在保持预训练结构的同时，对语言和签署变异具有鲁棒性。为填补这一空白，我们提出了分层自适应迁移学习框架，该框架根据训练表现行为逐步动态解冻预训练层。HATL结合了动态解冻、逐层学习率衰减和稳定性机制，在适应手语特征的同时保留通用表示。我们使用预训练的ST-GCN++骨干网络进行特征提取，并采用Transformer及自适应Transformer进行翻译，在Sign2Text和Sign2Gloss2Text翻译任务上评估HATL。为确保稳健的多语言泛化能力，我们在三个数据集上评估该方法：RWTH-PHOENIX-Weather-2014（PHOENIX14T）、Isharah和MedASL。实验结果表明，HATL在各项任务和模型中均持续优于传统迁移学习方法，其中ADAT在PHOENIX14T和Isharah上实现了15.0%的BLEU-4提升，在MedASL上实现了37.6%的提升。
URL
https://arxiv.org/abs/2603.19260
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Align then Adapt: Rethinking Parameter-Efficient Transfer Learning in 4D Perception
2026-02-26 14:58:59Yiding Sun, Jihua Zhu, Haozhe Cheng, Chaoyi Lu, Zhichuan Yang, Lin Chen, Yaonan WangAbstract
Point cloud video understanding is critical for robotics as it accurately encodes motion and scene interaction. We recognize that 4D datasets are far scarcer than 3D ones, which hampers the scalability of self-supervised 4D models. A promising alternative is to transfer 3D pre-trained models to 4D perception tasks. However, rigorous empirical analysis reveals two critical limitations that impede transfer capability: overfitting and the modality gap. To overcome these challenges, we develop a novel "Align then Adapt" (PointATA) paradigm that decomposes parameter-efficient transfer learning into two sequential stages. Optimal-transport theory is employed to quantify the distributional discrepancy between 3D and 4D datasets, enabling our proposed point align embedder to be trained in Stage 1 to alleviate the underlying modality gap. To mitigate overfitting, an efficient point-video adapter and a spatial-context encoder are integrated into the frozen 3D backbone to enhance temporal modeling capacity in Stage 2. Notably, with the above engineering-oriented designs, PointATA enables a pre-trained 3D model without temporal knowledge to reason about dynamic video content at a smaller parameter cost compared to previous work. Extensive experiments show that PointATA can match or even outperform strong full fine-tuning models, whilst enjoying the advantage of parameter efficiency, e.g. 97.21 \% accuracy on 3D action recognition, $+8.7 \%$ on 4 D action segmentation, and 84.06\% on 4D semantic segmentation.
Abstract (translated)
点云视频理解在机器人技术中至关重要，因为它能准确地编码运动和场景交互。我们认识到，相较于3D数据集而言，4D数据集稀缺得多，这阻碍了自监督4D模型的可扩展性。一种有前景的替代方案是从3D预训练模型迁移至4D感知任务。然而，严格的实证分析揭示出两种关键限制，即过度拟合和模态差距问题，这些问题阻碍了迁移能力。 为了解决这些挑战，我们开发了一种新颖的“对齐后适应”（PointATA）范式，将参数效率高的迁移学习分解成两个连续阶段。利用最优传输理论来量化3D与4D数据集之间的分布差异，使我们的提议点对齐嵌入器能够在第一阶段训练以缓解潜在的模态差距问题。为减轻过度拟合，在第二阶段集成高效的点视频适配器和空间上下文编码器到冻结的3D主干中，从而增强时间建模能力。 值得注意的是，通过上述工程设计，PointATA使一个无时间知识的预训练3D模型能够以较小的参数成本推理出动态视频内容。广泛的实验表明，PointATA可以与强大的全微调模型相匹配甚至超越其性能，同时享受参数效率的优势，例如在3D动作识别中达到97.21%的准确率，在4D动作分割中提高8.7%，以及在4D语义分割中达到84.06%的成绩。
URL
https://arxiv.org/abs/2602.23069
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FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics
2026-02-26 10:05:01Yunhua Zhong, Yixuan Tang, Yifan Li, Jie Yang, Pan Liu, Jun XiaAbstract
The identification and property prediction of chemical molecules is of central importance in the advancement of drug discovery and material science, where the tandem mass spectrometry technology gives valuable fragmentation cues in the form of mass-to-charge ratio peaks. However, the lack of experimental spectra hinders the attachment of each molecular identification, and thus urges the establishment of prediction approaches for computational models. Deep learning models appear promising for predicting molecular structure spectra, but overall assessment remains challenging as a result of the heterogeneity in methods and the lack of well-defined benchmarks. To address this, our contribution is the creation of benchmark framework FlexMS for constructing and evaluating diverse model architectures in mass spectrum prediction. With its easy-to-use flexibility, FlexMS supports the dynamic construction of numerous distinct combinations of model architectures, while assessing their performance on preprocessed public datasets using different metrics. In this paper, we provide insights into factors influencing performance, including the structural diversity of datasets, hyperparameters like learning rate and data sparsity, pretraining effects, metadata ablation settings and cross-domain transfer learning analysis. This provides practical guidance in choosing suitable models. Moreover, retrieval benchmarks simulate practical identification scenarios and score potential matches based on predicted spectra.
Abstract (translated)
化学分子的识别和性质预测在药物发现和材料科学的进步中至关重要。其中，串联质谱技术通过提供质量与电荷比（m/z）峰的形式提供了有价值的碎片线索。然而，由于缺乏实验光谱数据，每种分子的识别都受到了限制，并因此迫切需要建立用于计算模型的预测方法。深度学习模型在预测分子结构光谱方面显示出前景，但由于方法上的异质性和缺乏明确定义的标准，全面评估仍然具有挑战性。为了解决这个问题，我们的贡献是创建了基准框架FlexMS，该框架旨在构建和评估多种不同的架构以进行质量光谱预测。通过其易于使用的灵活性，FlexMS支持动态构建众多不同组合的模型架构，并在预处理后的公共数据集上使用不同的指标对其性能进行评估。 本文中，我们探讨了一些影响表现的因素，包括数据集中的结构多样性、超参数（如学习率和数据稀疏性）、预训练效果、元数据消融设置以及跨领域迁移学习分析。这为选择合适的模型提供了实用的指导。此外，检索基准模拟了实际识别场景，并根据预测光谱来评分潜在匹配项。 这个框架及其研究成果有助于促进更精确且高效的分子识别技术的发展，在药物研发和材料科学等领域具有重要意义。
URL
https://arxiv.org/abs/2602.22822
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Image-Based Classification of Olive Species Specific to Turkiye with Deep Neural Networks
2026-02-26 09:58:04Irfan Atabas, Hatice KaratasAbstract
In this study, image processing and deep learning methodologies were employed to automatically classify local olive species cultivated in Turkiye. A stereo camera was utilized to capture images of five distinct olive species, which were then preprocessed to ensure their suitability for analysis. Convolutional Neural Network (CNN) architectures, specifically MobileNetV2 and EfficientNetB0, were employed for image classification. These models were optimized through a transfer learning approach. The training and testing results indicated that the EfficientNetB0 model exhibited the optimal performance, with an accuracy of 94.5%. The findings demonstrate that deep learning-based systems offer an effective solution for classifying olive species with high accuracy. The developed method has significant potential for application in areas such as automatic identification and quality control of agricultural products.
Abstract (translated)
在这项研究中，使用了图像处理和深度学习的方法来自动分类在土耳其种植的地方橄榄品种。立体相机被用来拍摄五种不同的橄榄品种的图片，然后对这些图片进行预处理以确保它们适合分析。为了图像分类，采用了卷积神经网络（CNN）架构，具体来说是MobileNetV2和EfficientNetB0模型，并通过迁移学习的方法来优化这些模型。 训练和测试结果表明，EfficientNetB0模型表现出最佳性能，其准确率为94.5%。研究发现证明了基于深度学习的系统能够以高精度有效地对橄榄品种进行分类。开发出的方法在自动识别和农产品质量控制等领域具有重要的应用潜力。
URL
https://arxiv.org/abs/2603.00168
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Explainability-Aware Evaluation of Transfer Learning Models for IoT DDoS Detection Under Resource Constraints
Abstract
Distributed denial-of-service (DDoS) attacks threaten the availability of Internet of Things (IoT) infrastructures, particularly under resource-constrained deployment conditions. Although transfer learning models have shown promising detection accuracy, their reliability, computational feasibility, and interpretability in operational environments remain insufficiently explored. This study presents an explainability-aware empirical evaluation of seven pre-trained convolutional neural network architectures for multi-class IoT DDoS detection using the CICDDoS2019 dataset and an image-based traffic representation. The analysis integrates performance metrics, reliability-oriented statistics (MCC, Youden Index, confidence intervals), latency and training cost assessment, and interpretability evaluation using Grad-CAM and SHAP. Results indicate that DenseNet and MobileNet-based architectures achieve strong detection performance while demonstrating superior reliability and compact, class-consistent attribution patterns. DenseNet169 offers the strongest reliability and interpretability alignment, whereas MobileNetV3 provides an effective latency-accuracy trade-off for fog-level deployment. The findings emphasize the importance of combining performance, reliability, and explainability criteria when selecting deep learning models for IoT DDoS detection.
Abstract (translated)
分布式拒绝服务（DDoS）攻击威胁到物联网（IoT）基础设施的可用性，尤其是在资源受限的部署条件下。虽然迁移学习模型展示了令人鼓舞的检测准确率，但在操作环境中的可靠性、计算可行性及可解释性方面仍存在不足之处。本研究通过对CICDDoS2019数据集和基于图像的流量表示进行多类IoT DDoS检测，对七种预训练卷积神经网络架构进行了具有解释性的实证评估。该分析结合了性能指标、面向可靠性的统计量（MCC，Youden指数，置信区间），延迟及训练成本评估，以及使用Grad-CAM和SHAP进行的可解释性评价。结果显示，DenseNet和MobileNet架构在检测表现方面表现出色，并展示了卓越的可靠性与紧凑且类别一致的属性模式。DenseNet169提供了最强的可靠性和可解释性的契合度，而MobileNetV3则为雾计算级别的部署提供了一个有效的延迟-准确性权衡方案。研究结果强调了在选择用于IoT DDoS检测的深度学习模型时，应综合考虑性能、可靠性及可解释性标准的重要性。
URL
https://arxiv.org/abs/2602.22488
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Are Foundation Models the Route to Full-Stack Transfer in Robotics?
2026-02-25 15:19:44Freek Stulp, Samuel Bustamante, Jo\~ao Silv\'erio, Alin Albu-Sch\"affer, Jeannette Bohg, Shuran SongAbstract
In humans and robots alike, transfer learning occurs at different levels of abstraction, from high-level linguistic transfer to low-level transfer of motor skills. In this article, we provide an overview of the impact that foundation models and transformer networks have had on these different levels, bringing robots closer than ever to "full-stack transfer". Considering LLMs, VLMs and VLAs from a robotic transfer learning perspective allows us to highlight recurring concepts for transfer, beyond specific implementations. We also consider the challenges of data collection and transfer benchmarks for robotics in the age of foundation models. Are foundation models the route to full-stack transfer in robotics? Our expectation is that they will certainly stay on this route as a key technology.
Abstract (translated)
在人类和机器人中，迁移学习发生在不同层次的抽象层面，从高层次的语言转换到低层次的动作技能转移。本文概述了基础模型和变压器网络对这些不同层次的影响，使机器人比以往任何时候都更接近“全栈迁移”。从机器人迁移学习的角度考虑大语言模型（LLMs）、视觉语言模型（VLMs）以及视觉语言助手（VLAs），可以帮助我们突出超越特定实现的迁移概念。我们还探讨了在基础模型时代收集数据和建立转移基准给机器人技术带来的挑战。基础模型是否是通往机器人全栈迁移的道路？我们的预期是，它们无疑将继续作为关键技术沿着这一路径发展。
URL
https://arxiv.org/abs/2602.22001
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TIRAuxCloud: A Thermal Infrared Dataset for Day and Night Cloud Detection
2026-02-25 13:36:10Alexis Apostolakis, Vasileios Botsos, Niklas W\"olki, Andrea Spichtinger, Nikolaos Ioannis Bountos, Ioannis Papoutsis, Panayiotis TsanakasAbstract
Clouds are a major obstacle in Earth observation, limiting the usability and reliability of critical remote sensing applications such as fire disaster response, urban heat island monitoring, and snow and ice cover mapping. Therefore, the ability to detect clouds 24/7 is of paramount importance. While visible and near-infrared bands are effective for daytime cloud detection, their dependence on solar illumination makes them unsuitable for nighttime monitoring. In contrast, thermal infrared (TIR) imagery plays a crucial role in detecting clouds at night, when sunlight is absent. Due to their generally lower temperatures, clouds emit distinct thermal signatures that are detectable in TIR bands. Despite this, accurate nighttime cloud detection remains challenging due to limited spectral information and the typically lower spatial resolution of TIR imagery. To address these challenges, we present TIRAuxCloud, a multi-modal dataset centered around thermal spectral data to facilitate cloud segmentation under both daytime and nighttime conditions. The dataset comprises a unique combination of multispectral data (TIR, optical, and near-infrared bands) from Landsat and VIIRS, aligned with auxiliary information layers. Elevation, land cover, meteorological variables, and cloud-free reference images are included to help reduce surface-cloud ambiguity and cloud formation uncertainty. To overcome the scarcity of manual cloud labels, we include a large set of samples with automated cloud masks and a smaller manually annotated subset to further evaluate and improve models. Comprehensive benchmarks are presented to establish performance baselines through supervised and transfer learning, demonstrating the dataset's value in advancing the development of innovative methods for day and night time cloud detection.
Abstract (translated)
云是地球观测中的主要障碍，限制了关键遥感应用（如火灾灾害响应、城市热岛监测和雪冰覆盖制图）的可用性和可靠性。因此，24/7全天候检测云的能力至关重要。虽然可见光和近红外波段在白天有效用于检测云层，但它们依赖于太阳光照的特点使它们不适合夜间监控。相比之下，在没有阳光的情况下，热红外（TIR）影像在夜间云层检测中发挥着关键作用。由于通常温度较低，云层在TIR波段表现出独特的热特征，这些特征可以被探测到。然而，准确的夜间云层检测仍然具有挑战性，因为可用的光谱信息有限，并且TIR图像的空间分辨率一般较低。 为了解决这些问题，我们提出了一个以热光谱数据为中心的多模态数据集 TIRAuxCloud，旨在促进在白天和夜晚条件下的云分割。该数据集结合了来自 Landsat 和 VIIRS 的多光谱数据（包括 TIR、光学和近红外波段），并配有辅助信息层。地形高度、土地覆盖、气象变量以及无云参考图像被包含进来以帮助减少地表与云之间的混淆，并降低云形成不确定性。 为了克服手动标注的不足，我们提供了一大批带有自动云掩模的数据样本，以及一个较小的手动注释子集，用于进一步评估和改进模型。通过监督学习和迁移学习建立了全面基准测试，展示了该数据集在促进日间与夜间云层检测创新方法开发方面的价值。
URL
https://arxiv.org/abs/2602.21905
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Cross domain Persistent Monitoring for Hybrid Aerial Underwater Vehicles
2026-02-23 20:56:21Ricardo B. Grando, Victor A. Kich, Alisson H. Kolling, Junior C. D. Jesus, Rodrigo S. Guerra, Paulo L. J. Drews-JrAbstract
Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs) have emerged as platforms capable of operating in both aerial and underwater environments, enabling applications such as inspection, mapping, search, and rescue in challenging scenarios. However, the development of novel methodologies poses significant challenges due to the distinct dynamics and constraints of the air and water domains. In this work, we present persistent monitoring tasks for HUAUVs by combining Deep Reinforcement Learning (DRL) and Transfer Learning to enable cross-domain adaptability. Our approach employs a shared DRL architecture trained on Lidar sensor data (on air) and Sonar data (underwater), demonstrating the feasibility of a unified policy for both environments. We further show that the methodology presents promising results, taking into account the uncertainty of the environment and the dynamics of multiple mobile targets. The proposed framework lays the groundwork for scalable autonomous persistent monitoring solutions based on DRL for hybrid aerial-underwater vehicles.
Abstract (translated)
混合无人空中水下车辆（HUAUVs）作为能够在空域和水域环境中运行的平台已经出现，使诸如检查、测绘、搜索与救援等应用在复杂场景中成为可能。然而，由于空气和水环境的独特动态特性和约束条件，开发新型方法面临着重大挑战。在这项工作中，我们通过结合深度强化学习（DRL）和迁移学习来提出一种适用于HUAUVs的持续监测任务的方法，以实现跨域适应性。我们的方法采用了一种共享的DRL架构，并利用激光雷达传感器数据（空中）和声纳数据（水下）进行训练，展示了针对这两种环境制定统一策略的可能性。 我们进一步表明，所提出的方法在考虑到环境不确定性以及多个移动目标的动力学情况下，能够取得令人鼓舞的结果。该框架为基于DRL的混合空海无人车辆的大规模自主持续监控解决方案奠定了基础。
URL
https://arxiv.org/abs/2602.21259
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Hierarchical Molecular Representation Learning via Fragment-Based Self-Supervised Embedding Prediction
2026-02-23 20:41:44Jiele Wu, Haozhe Ma, Zhihan Guo, Thanh Vinh Vo, Tze Yun LeongAbstract
Graph self-supervised learning (GSSL) has demonstrated strong potential for generating expressive graph embeddings without the need for human annotations, making it particularly valuable in domains with high labeling costs such as molecular graph analysis. However, existing GSSL methods mostly focus on node- or edge-level information, often ignoring chemically relevant substructures which strongly influence molecular properties. In this work, we propose Graph Semantic Predictive Network (GraSPNet), a hierarchical self-supervised framework that explicitly models both atomic-level and fragment-level semantics. GraSPNet decomposes molecular graphs into chemically meaningful fragments without predefined vocabularies and learns node- and fragment-level representations through multi-level message passing with masked semantic prediction at both levels. This hierarchical semantic supervision enables GraSPNet to learn multi-resolution structural information that is both expressive and transferable. Extensive experiments on multiple molecular property prediction benchmarks demonstrate that GraSPNet learns chemically meaningful representations and consistently outperforms state-of-the-art GSSL methods in transfer learning settings.
Abstract (translated)
图自监督学习（GSSL）在生成无需人工标注的富有表现力的图嵌入方面表现出巨大潜力，特别是在分子图分析等标签成本高昂的应用领域中尤其有价值。然而，现有的大多数GSSL方法主要关注节点级或边级信息，往往忽略了强烈影响分子属性的化学相关子结构。在此研究中，我们提出了Graph Semantic Predictive Network（GraSPNet），这是一种层次化的自监督框架，它显式地对原子级和片段级语义进行建模。GraSPNet将分子图分解为具有化学意义的片段，并且不依赖预定义词汇表，在多层级信息传递过程中通过在节点级别和片段级别的掩码语义预测学习节点表示和片段表示。这种层次化的语义监督使得GraSPNet能够从数据中学习到兼具表达性和可迁移性的多层次结构信息。 在多个分子属性预测基准测试中的广泛实验表明，GraSPNet学到了具有化学意义的表征，并且在迁移学习设置下始终优于最先进的GSSL方法。
URL
https://arxiv.org/abs/2602.20344
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Initialization matters in few-shot adaptation of vision-language models for histopathological image classification
2026-02-21 09:08:40Pablo Meseguer, Roc\'io del Amor, Valery NaranjoAbstract
Vision language models (VLM) pre-trained on datasets of histopathological image-caption pairs enabled zero-shot slide-level classification. The ability of VLM image encoders to extract discriminative features also opens the door for supervised fine-tuning for whole-slide image (WSI) classification, ideally using few labeled samples. Slide-level prediction frameworks require the incorporation of multiple instance learning (MIL) due to the gigapixel size of the WSI. Following patch-level feature extraction and aggregation, MIL frameworks rely on linear classifiers trained on top of the slide-level aggregated features. Classifier weight initialization has a large influence on Linear Probing performance in efficient transfer learning (ETL) approaches based on few-shot learning. In this work, we propose Zero-Shot Multiple-Instance Learning (ZS-MIL) to address the limitations of random classifier initialization that underperform zero-shot prediction in MIL problems. ZS-MIL uses the class-level embeddings of the VLM text encoder as the classification layer's starting point to compute each sample's bag-level probabilities. Through multiple experiments, we demonstrate the robustness of ZS-MIL compared to well-known weight initialization techniques both in terms of performance and variability in an ETL few-shot scenario for subtyping prediction.
Abstract (translated)
基于病理图像-描述对数据集预训练的视觉语言模型（VLM）实现了零样本幻灯片级别分类。VLM图像编码器提取判别性特征的能力也为监督微调用于全滑片图像（WSI）分类打开了大门，理想情况下使用少量标注样本。由于WSI的千兆像素大小，幻灯片级别的预测框架需要融入多实例学习（MIL）。在基于补丁级别特征提取和聚合之后，MIL框架依赖于在幻灯片级别聚合特征上训练的线性分类器。分类权重初始化对基于少样本学习的有效迁移学习（ETL）方法中的线性探测性能有很大影响。在这项工作中，我们提出了零样本多实例学习（ZS-MIL），以解决随机分类器初始设置在MIL问题中表现不佳于零样本预测的局限性。ZS-MIL使用VLM文本编码器的类别级嵌入作为计算每个样本包级别概率的起始点。通过多个实验，我们展示了与已知权重初始化技术相比，在ETL少样本场景下子类型预测中的性能和变异性方面，ZS-MIL的鲁棒性。
URL
https://arxiv.org/abs/2602.18766
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Quantum-enhanced satellite image classification
2026-02-20 17:02:16Qi Zhang, Anton Simen, Carlos Flores-Garrig\'os, Gabriel Alvarado Barrios, Paolo A. Erdman, Enrique Solano, Aaron C. Kemp, Vincent Beltrani, Vedangi Pathak, Hamed MohammadbagherpoorAbstract
We demonstrate the application of a quantum feature extraction method to enhance multi-class image classification for space applications. By harnessing the dynamics of many-body spin Hamiltonians, the method generates expressive quantum features that, when combined with classical processing, lead to quantum-enhanced classification accuracy. Using a strong and well-established ResNet50 baseline, we achieved a maximum classical accuracy of 83%, which can be improved to 84% with a transfer learning approach. In contrast, applying our quantum-classical method the performance is increased to 87% accuracy, demonstrating a clear and reproducible improvement over robust classical approaches. Implemented on several of IBM's quantum processors, our hybrid quantum-classical approach delivers consistent gains of 2-3% in absolute accuracy. These results highlight the practical potential of current and near-term quantum processors in high-stakes, data-driven domains such as satellite imaging and remote sensing, while suggesting broader applicability in real-world machine learning tasks.
Abstract (translated)
我们演示了量子特征提取方法在增强空间应用中多类图像分类中的应用。通过利用多体自旋哈密顿量的动力学，该方法生成具有表达力的量子特征，当与经典处理结合时，可以提高分类准确率。使用强大而稳定的ResNet50基线模型，我们达到了最大83%的经典准确性，并且可以通过迁移学习的方法将这一数值提升至84%。相比之下，应用我们的量子-经典方法后，性能提升到了87%，这表明在稳健的经典方法上实现了明确可重复的改进。该方法在IBM的各种量子处理器上实现，混合量子和经典方法在整个准确率方面带来了2-3%的绝对改善。这些结果突显了当前及近期量子处理器在卫星成像和遥感等高风险数据驱动领域中的实际潜力，并暗示其在现实世界的机器学习任务中具有更广泛的应用前景。
URL
https://arxiv.org/abs/2602.18350
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Image-Based Classification of Olive Varieties Native to Turkiye Using Multiple Deep Learning Architectures: Analysis of Performance, Complexity, and Generalization
2026-02-20 07:26:11Hatice Karatas, Irfan AtabasAbstract
This study compares multiple deep learning architectures for the automated, image-based classification of five locally cultivated black table olive varieties in Turkey: Gemlik, Ayvalik, Uslu, Erkence, and Celebi. Using a dataset of 2500 images, ten architectures - MobileNetV2, EfficientNetB0, EfficientNetV2-S, ResNet50, ResNet101, DenseNet121, InceptionV3, ConvNeXt-Tiny, ViT-B16, and Swin-T - were trained using transfer learning. Model performance was evaluated using accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Cohen's Kappa, ROC-AUC, number of parameters, FLOPs, inference time, and generalization gap. EfficientNetV2-S achieved the highest classification accuracy (95.8%), while EfficientNetB0 provided the best trade-off between accuracy and computational complexity. Overall, the results indicate that under limited data conditions, parametric efficiency plays a more critical role than model depth alone.
Abstract (translated)
这项研究比较了多种深度学习架构，用于在土耳其自动分类五种当地栽培的黑橄榄品种：Gemlik、Ayvalik、Uslu、Erkence和Celebi。使用包含2500张图像的数据集，十种架构——MobileNetV2、EfficientNetB0、EfficientNetV2-S、ResNet50、ResNet101、DenseNet121、InceptionV3、ConvNeXt-Tiny、ViT-B16和Swin-T，在迁移学习的背景下进行了训练。模型性能通过准确性、精确性、召回率、F1分数、马修斯相关系数（MCC）、卡帕值、ROC-AUC指标、参数数量、浮点运算数（FLOPs）、推理时间和泛化差距进行评估。EfficientNetV2-S实现了最高的分类准确率（95.8%），而EfficientNetB0在准确性与计算复杂度之间提供了最佳的平衡。总体而言，研究结果表明，在数据受限的情况下，参数效率比模型深度本身扮演着更重要的角色。
URL
https://arxiv.org/abs/2602.18530
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A Novel Approach for Testing Water Safety Using Deep Learning Inference of Microscopic Images of Unincubated Water Samples
2026-02-20 01:07:42Sanjay SrinivasanAbstract
Fecal-contaminated water causes diseases and even death. Current microbial water safety tests require pathogen incubation, taking 24-72 hours and costing \$20-\$50 per test. This paper presents a solution (DeepScope) exceeding UNICEF's ideal Target Product Profile requirements for presence/absence testing, with an estimated per-test cost of \$0.44. By eliminating the need for pathogen incubation, DeepScope reduces testing time by over 98\%. In DeepScope, a dataset of microscope images of bacteria and water samples was assembled. An innovative augmentation technique, generating up to 21 trillion images from a single microscope image, was developed. Four convolutional neural network models were developed using transfer learning and regularization techniques, then evaluated on a field-test dataset comprising 100,000 microscope images of unseen, real-world water samples collected from fourteen different water sources across Sammamish, WA. Precision-recall analysis showed the DeepScope model achieves 93\% accuracy, with precision of 90\% and recall exceeding 94\%. The DeepScope model was deployed on a web server, and mobile applications for Android and iOS were developed, enabling Internet-based or smartphone-based water safety testing, with results obtained in seconds.
Abstract (translated)
受粪便污染的水会导致疾病甚至死亡。当前用于微生物水质安全测试的方法需要病原体培养，耗时24至72小时，并且每次检测的成本在20至50美元之间。本文介绍了一种解决方案（DeepScope），该方案超过了联合国儿童基金会理想的产品性能指标要求，能够进行存在与否的测试，并且估计每项测试的成本为0.44美元。通过消除对病原体培养的需求，DeepScope将检测时间减少了超过98%。 在DeepScope中，一个包含细菌和水样显微镜图像的数据集被构建起来。开发了一种创新的增强技术，可以从单个显微镜图像生成多达21万亿张新图像。使用迁移学习和正则化技术开发了四个卷积神经网络模型，并通过一个现场测试数据集进行了评估，该数据集包括从华盛顿州萨马米什市十四处不同水源收集到的十万张显微镜图像。 精确度-召回率分析表明，DeepScope模型能够实现93%的整体准确率、90%的精确度和超过94%的召回率。DeepScope模型部署在了网络服务器上，并为Android和iOS开发了移动应用，实现了基于互联网或智能手机的水质安全检测，能够在几秒钟内获得测试结果。
URL
https://arxiv.org/abs/2603.06611
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Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark
2026-02-18 19:15:30Charalampos Mastrokostas, Nikolaos Giarelis, Nikos KaracapilidisAbstract
Recent advancements in Natural Language Processing and Deep Learning have enabled the development of Large Language Models (LLMs), which have significantly advanced the state-of-the-art across a wide range of tasks, including Question Answering (QA). Despite these advancements, research on LLMs has primarily targeted high-resourced languages (e.g., English), and only recently has attention shifted toward multilingual models. However, these models demonstrate a training data bias towards a small number of popular languages or rely on transfer learning from high- to under-resourced languages; this may lead to a misrepresentation of social, cultural, and historical aspects. To address this challenge, monolingual LLMs have been developed for under-resourced languages; however, their effectiveness remains less studied when compared to multilingual counterparts on language-specific tasks. In this study, we address this research gap in Greek QA by contributing: (i) DemosQA, a novel dataset, which is constructed using social media user questions and community-reviewed answers to better capture the Greek social and cultural zeitgeist; (ii) a memory-efficient LLM evaluation framework adaptable to diverse QA datasets and languages; and (iii) an extensive evaluation of 11 monolingual and multilingual LLMs on 6 human-curated Greek QA datasets using 3 different prompting strategies. We release our code and data to facilitate reproducibility.
Abstract (translated)
URL
https://arxiv.org/abs/2602.16811
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Cross-household Transfer Learning Approach with LSTM-based Demand Forecasting
2026-02-15 18:35:14Manal Rahal, Bestoun S. Ahmed, Roger Renstr\"om, Robert StenerAbstract
With the rapid increase in residential heat pump (HP) installations, optimizing hot water production in households is essential, yet it faces major technical and scalability challenges. Adapting production to actual household needs requires accurate forecasting of hot water demand to ensure comfort and, most importantly, to reduce energy waste. However, the conventional approach of training separate machine learning models for each household becomes computationally expensive at scale, particularly in cloud-connected HP deployments. This study introduces DELTAiF, a transfer learning (TL) based framework that provides scalable and accurate prediction of household hot water consumption. By predicting large hot water usage events, such as showers, DELTAiF enables adaptive yet scalable hot water production at the household level. DELTAiF leverages learned knowledge from a representative household and fine-tunes it across others, eliminating the need to train separate machine learning models for each HP installation. This approach reduces overall training time by approximately 67 percent while maintaining high predictive accuracy values between 0.874 and 0.991, and mean absolute percentage error values between 0.001 and 0.017. The results show that TL is particularly effective when the source household exhibits regular consumption patterns, enabling hot water demand forecasting at scale.
Abstract (translated)
URL
https://arxiv.org/abs/2602.14267
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Human-Aligned Evaluation of a Pixel-wise DNN Color Constancy Model
2026-02-14 21:03:29Hamed Heidari-Gorji, Raquel Gil Rodriguez, Karl R. GegenfurtnerAbstract
We previously investigated color constancy in photorealistic virtual reality (VR) and developed a Deep Neural Network (DNN) that predicts reflectance from rendered images. Here, we combine both approaches to compare and study a model and human performance with respect to established color constancy mechanisms: local surround, maximum flux and spatial mean. Rather than evaluating the model against physical ground truth, model performance was assessed using the same achromatic object selection task employed in the human experiments. The model, a ResNet based U-Net from our previous work, was pre-trained on rendered images to predict surface reflectance. We then applied transfer learning, fine-tuning only the network's decoder on images from the baseline VR condition. To parallel the human experiment, the model's output was used to perform the same achromatic object selection task across all conditions. Results show a strong correspondence between the model and human behavior. Both achieved high constancy under baseline conditions and showed similar, condition-dependent performance declines when the local surround or spatial mean color cues were removed.
Abstract (translated)
URL
https://arxiv.org/abs/2602.13887
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Using Machine Learning to Enhance the Detection of Obfuscated Abusive Words in Swahili: A Focus on Child Safety
2026-02-13 21:02:14Phyllis Nabangi, Abdul-Jalil Zakaria, Jema David NdibwileAbstract
The rise of digital technology has dramatically increased the potential for cyberbullying and online abuse, necessitating enhanced measures for detection and prevention, especially among children. This study focuses on detecting abusive obfuscated language in Swahili, a low-resource language that poses unique challenges due to its limited linguistic resources and technological support. Swahili is chosen due to its popularity and being the most widely spoken language in Africa, with over 16 million native speakers and upwards of 100 million speakers in total, spanning regions in East Africa and some parts of the Middle East. We employed machine learning models including Support Vector Machines (SVM), Logistic Regression, and Decision Trees, optimized through rigorous parameter tuning and techniques like Synthetic Minority Over-sampling Technique (SMOTE) to handle data imbalance. Our analysis revealed that, while these models perform well in high-dimensional textual data, our dataset's small size and imbalance limit our findings' generalizability. Precision, recall, and F1 scores were thoroughly analyzed, highlighting the nuanced performance of each model in detecting obfuscated language. This research contributes to the broader discourse on ensuring safer online environments for children, advocating for expanded datasets and advanced machine-learning techniques to improve the effectiveness of cyberbullying detection systems. Future work will focus on enhancing data robustness, exploring transfer learning, and integrating multimodal data to create more comprehensive and culturally sensitive detection mechanisms.
Abstract (translated)
URL
https://arxiv.org/abs/2602.13455
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3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset
2026-02-13 11:08:15Mehran Advand, Zahra Dehghanian, Navid Faraji, Reza Barati, Seyed Amir Ahmad Safavi-Naini, Hamid R. RabieeAbstract
Existing medical imaging datasets for abdominal CT often lack three-dimensional annotations, multi-organ coverage, or precise lesion-to-organ associations, hindering robust representation learning and clinical applications. To address this gap, we introduce 3DLAND, a large-scale benchmark dataset comprising over 6,000 contrast-enhanced CT volumes with over 20,000 high-fidelity 3D lesion annotations linked to seven abdominal organs: liver, kidneys, pancreas, spleen, stomach, and gallbladder. Our streamlined three-phase pipeline integrates automated spatial reasoning, prompt-optimized 2D segmentation, and memory-guided 3D propagation, validated by expert radiologists with surface dice scores exceeding 0.75. By providing diverse lesion types and patient demographics, 3DLAND enables scalable evaluation of anomaly detection, localization, and cross-organ transfer learning for medical AI. Our dataset establishes a new benchmark for evaluating organ-aware 3D segmentation models, paving the way for advancements in healthcare-oriented AI. To facilitate reproducibility and further research, the 3DLAND dataset and implementation code are publicly available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2602.12820
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A Lightweight and Explainable DenseNet-121 Framework for Grape Leaf Disease Classification
2026-02-12 23:56:08Md. Ehsanul Haque, Md. Saymon Hosen Polash, Rakib Hasan Ovi, Aminul Kader Bulbul, Md Kamrul Siam, Tamim Hasan SaykatAbstract
Grapes are among the most economically and culturally significant fruits on a global scale, and table grapes and wine are produced in significant quantities in Europe and Asia. The production and quality of grapes are significantly impacted by grape diseases such as Bacterial Rot, Downy Mildew, and Powdery Mildew. Consequently, the sustainable management of a vineyard necessitates the early and precise identification of these diseases. Current automated methods, particularly those that are based on the YOLO framework, are often computationally costly and lack interpretability that makes them unsuitable for real-world scenarios. This study proposes grape leaf disease classification using Optimized DenseNet 121. Domain-specific preprocessing and extensive connectivity reveal disease-relevant characteristics, including veins, edges, and lesions. An extensive comparison with baseline CNN models, including ResNet18, VGG16, AlexNet, and SqueezeNet, demonstrates that the proposed model exhibits superior performance. It achieves an accuracy of 99.27%, an F1 score of 99.28%, a specificity of 99.71%, and a Kappa of 98.86%, with an inference time of 9 seconds. The cross-validation findings show a mean accuracy of 99.12%, indicating strength and generalizability across all classes. We also employ Grad-CAM to highlight disease-related regions to guarantee the model is highlighting physiologically relevant aspects and increase transparency and confidence. Model optimization reduces processing requirements for real-time deployment, while transfer learning ensures consistency on smaller and unbalanced samples. An effective architecture, domain-specific preprocessing, and interpretable outputs make the proposed framework scalable, precise, and computationally inexpensive for detecting grape leaf diseases.
Abstract (translated)
URL
https://arxiv.org/abs/2602.12484
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A Controlled Study of Double DQN and Dueling DQN Under Cross-Environment Transfer
2026-02-11 11:47:36Azkaa Nasir, Fatima Dossa, Muhammad Ahmed Atif, Mohammad Shahid ShaikhAbstract
Transfer learning in deep reinforcement learning is often motivated by improved stability and reduced training cost, but it can also fail under substantial domain shift. This paper presents a controlled empirical study examining how architectural differences between Double Deep Q-Networks (DDQN) and Dueling DQN influence transfer behavior across environments. Using CartPole as a source task and LunarLander as a structurally distinct target task, we evaluate a fixed layer-wise representation transfer protocol under identical hyperparameters and training conditions, with baseline agents trained from scratch used to contextualize transfer effects. Empirical results show that DDQN consistently avoids negative transfer under the examined setup and maintains learning dynamics comparable to baseline performance in the target environment. In contrast, Dueling DQN consistently exhibits negative transfer under identical conditions, characterized by degraded rewards and unstable optimization behavior. Statistical analysis across multiple random seeds confirms a significant performance gap under transfer. These findings suggest that architectural inductive bias is strongly associated with robustness to cross-environment transfer in value-based deep reinforcement learning under the examined transfer protocol.
Abstract (translated)
深度强化学习中的迁移学习通常由改进的稳定性以及降低训练成本所驱动，但在显著的任务域变化下可能会失败。本文通过一项受控实证研究，探讨了双层深度Q网络（DDQN）和斗士DQN之间架构差异如何影响跨环境中的迁移行为。使用CartPole作为源任务，并将LunarLander作为一个结构上明显不同的目标任务，在相同的超参数和训练条件下评估了一个固定的逐层表示迁移协议。我们还利用从头开始训练的基线智能体来定位迁移效果。实证结果表明，DDQN在观察到的设置下始终避免了负向迁移，并且其学习动态与目标环境中的基线性能相当。相比之下，在相同的条件下，斗士DQN则持续表现出负面迁移，表现为奖励下降和优化行为不稳定。多个随机种子上的统计分析确认了转移过程中的显著性能差距。这些发现表明，在所研究的迁移协议下，基于价值的深度强化学习中架构性的归纳偏差与跨环境迁移时的鲁棒性之间存在强相关性。
URL
https://arxiv.org/abs/2602.09810
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