Tootfinder

Bridged Clustering for Representation Learning: Semi-Supervised Sparse Bridging
Patrick Peixuan Ye, Chen Shani, Ellen Vitercik
https://arxiv.org/abs/2510.07182 https://

Bridged Clustering for Representation Learning: Semi-Supervised Sparse Bridging
We introduce Bridged Clustering, a semi-supervised framework to learn predictors from any unpaired input $X$ and output $Y$ dataset. Our method first clusters $X$ and $Y$ independently, then learns a sparse, interpretable bridge between clusters using only a few paired examples. At inference, a new input $x$ is assigned to its nearest input cluster, and the centroid of the linked output cluster is returned as the prediction $\hat{y}$. Unlike traditional SSL, Bridged Clustering explicitly levera…

Leveraging Information Divergence for Robust Semi-Supervised Fetal Ultrasound Image Segmentation
Fangyijie Wang, Gu\'enol\'e Silvestre, Kathleen M. Curran
https://arxiv.org/abs/2509.06495

Leveraging Information Divergence for Robust Semi-Supervised Fetal Ultrasound Image Segmentation
Maternal-fetal Ultrasound is the primary modality for monitoring fetal development, yet automated segmentation remains challenging due to the scarcity of high-quality annotations. To address this limitation, we propose a semi-supervised learning framework that leverages information divergence for robust fetal ultrasound segmentation. Our method employs a lightweight convolutional network (1.47M parameters) and a Transformer-based network, trained jointly with labelled data through standard supe…

Semi-supervised Deep Transfer for Regression without Domain Alignment
Mainak Biswas, Ambedkar Dukkipati, Devarajan Sridharan
https://arxiv.org/abs/2509.05092 https://

Semi-supervised Deep Transfer for Regression without Domain Alignment
Deep learning models deployed in real-world applications (e.g., medicine) face challenges because source models do not generalize well to domain-shifted target data. Many successful domain adaptation (DA) approaches require full access to source data. Yet, such requirements are unrealistic in scenarios where source data cannot be shared either because of privacy concerns or because it is too large and incurs prohibitive storage or computational costs. Moreover, resource constraints may limit th…

Enhancement of Quantum Semi-Supervised Learning via Improved Laplacian and Poisson Methods
Hamed Gholipour, Farid Bozorgnia, Hamzeh Mohammadigheymasi, Kailash Hambarde, Javier Mancilla, Hugo Proenca, Joao Neves, Moharram Challenger
https://arxiv.org/abs/2508.02054

Enhancement of Quantum Semi-Supervised Learning via Improved Laplacian and Poisson Methods
This paper develops a hybrid quantum approach for graph-based semi-supervised learning to enhance performance in scenarios where labeled data is scarce. We introduce two enhanced quantum models, the Improved Laplacian Quantum Semi-Supervised Learning (ILQSSL) and the Improved Poisson Quantum Semi-Supervised Learning (IPQSSL), that incorporate advanced label propagation strategies within variational quantum circuits. These models utilize QR decomposition to embed graph structure directly into qu…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/6]:
- Shaping Sparse Rewards in Reinforcement Learning: A Semi-supervised Approach
Wenyun Li, Wenjie Huang, Chen Sun

Pitch-Conditioned Instrument Sound Synthesis From an Interactive Timbre Latent Space
Christian Limberg, Fares Schulz, Zhe Zhang, Stefan Weinzierl
https://arxiv.org/abs/2510.04339

Pitch-Conditioned Instrument Sound Synthesis From an Interactive Timbre Latent Space
This paper presents a novel approach to neural instrument sound synthesis using a two-stage semi-supervised learning framework capable of generating pitch-accurate, high-quality music samples from an expressive timbre latent space. Existing approaches that achieve sufficient quality for music production often rely on high-dimensional latent representations that are difficult to navigate and provide unintuitive user experiences. We address this limitation through a two-stage training paradigm: f…

WOCD: A Semi-Supervised Method for Overlapping Community Detection Using Weak Cliques
Shaozhen Ma, Hanchen Wang, Dong Wen, Wenjie Zhang, Wei Huang, Ying Zhang
https://arxiv.org/abs/2508.00927

WOCD: A Semi-Supervised Method for Overlapping Community Detection Using Weak Cliques
Overlapping community detection (OCD) is a fundamental graph data analysis task for extracting graph patterns. Traditional OCD methods can be broadly divided into node clustering and link clustering approaches, both of which rely solely on link information to identify overlapping communities. In recent years, deep learning-based methods have made significant advancements for this task. However, existing GNN-based approaches often face difficulties in effectively integrating link, attribute, and…

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
Zhuocheng Liu, Zhishu Shen, Qiushi Zheng, Tiehua Zhang, Zheng Lei, Jiong Jin
https://arxiv.org/abs/2507.22339

A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
Low Earth Orbit (LEO) satellites are emerging as key components of 6G networks, with many already deployed to support large-scale Earth observation and sensing related tasks. Federated Learning (FL) presents a promising paradigm for enabling distributed intelligence in these resource-constrained and dynamic environments. However, achieving reliable convergence, while minimizing both processing time and energy consumption, remains a substantial challenge, particularly in heterogeneous and partia…

On the sample complexity of semi-supervised multi-objective learning
Tobias Wegel, Geelon So, Junhyung Park, Fanny Yang
https://arxiv.org/abs/2508.17152 https://

On the sample complexity of semi-supervised multi-objective learning
In multi-objective learning (MOL), several possibly competing prediction tasks must be solved jointly by a single model. Achieving good trade-offs may require a model class $\mathcal{G}$ with larger capacity than what is necessary for solving the individual tasks. This, in turn, increases the statistical cost, as reflected in known MOL bounds that depend on the complexity of $\mathcal{G}$. We show that this cost is unavoidable for some losses, even in an idealized semi-supervised setting, where…

Mouse-Guided Gaze: Semi-Supervised Learning of Intention-Aware Representations for Reading Detection
Seongsil Heo, Roberto Manduchi
https://arxiv.org/abs/2509.19574 https://

Mouse-Guided Gaze: Semi-Supervised Learning of Intention-Aware Representations for Reading Detection
Understanding user intent during magnified reading is critical for accessible interface design. Yet magnification collapses visual context and forces continual viewport dragging, producing fragmented, noisy gaze and obscuring reading intent. We present a semi-supervised framework that learns intention-aware gaze representations by leveraging mouse trajectories as weak supervision. The model is first pretrained to predict mouse velocity from unlabeled gaze, then fine-tuned to classify reading ve…

SpikeMatch: Semi-Supervised Learning with Temporal Dynamics of Spiking Neural Networks
Jini Yang, Beomseok Oh, Seungryong Kim, Sunok Kim
https://arxiv.org/abs/2509.22581 https:/…

SpikeMatch: Semi-Supervised Learning with Temporal Dynamics of Spiking Neural Networks
Spiking neural networks (SNNs) have recently been attracting significant attention for their biological plausibility and energy efficiency, but semi-supervised learning (SSL) methods for SNN-based models remain underexplored compared to those for artificial neural networks (ANNs). In this paper, we introduce SpikeMatch, the first SSL framework for SNNs that leverages the temporal dynamics through the leakage factor of SNNs for diverse pseudo-labeling within a co-training framework. By utilizing…

AI-Assisted Pleural Effusion Volume Estimation from Contrast-Enhanced CT Images
Sanhita Basu, Tomas Fr\"oding, Ali Teymur Kahraman, Dimitris Toumpanakis, Tobias Sj\"oblom
https://arxiv.org/abs/2510.03856

AI-Assisted Pleural Effusion Volume Estimation from Contrast-Enhanced CT Images
Background: Pleural Effusions (PE) is a common finding in many different clinical conditions, but accurately measuring their volume from CT scans is challenging. Purpose: To improve PE segmentation and quantification for enhanced clinical management, we have developed and trained a semi-supervised deep learning framework on contrast-enhanced CT volumes. Materials and Methods: This retrospective study collected CT Pulmonary Angiogram (CTPA) data from internal and external datasets. A subset of 1…

Semi-supervised classification of Stars, Galaxies and Quasars using K-means and Random Forest
Vahid Asadi, Hosein Haghi, Akram Hasani Zonoozi
https://arxiv.org/abs/2507.14072

Semi-supervised classification of Stars, Galaxies and Quasars using K-means and Random Forest
Classifying stars, galaxies, and quasars is essential for understanding cosmic structure and evolution, but the vast data from modern surveys make manual classification impractical, while supervised learning methods remain constrained by the scarcity of labeled spectroscopic data. We aim to develop a scalable, label-efficient method for astronomical classification by leveraging semi-supervised learning (SSL) to overcome the limitations of fully supervised approaches. We propose a novel SSL fram…

Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum
Agastya Raj, Zehao Wang, Tingjun Chen, Daniel C Kilper, Marco Ruffini
https://arxiv.org/abs/2507.21728

Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum
Accurate modeling of the gain spectrum in Erbium-Doped Fiber Amplifiers (EDFAs) is essential for optimizing optical network performance, particularly as networks evolve toward multi-vendor solutions. In this work, we propose a generalized few-shot transfer learning architecture based on a Semi-Supervised Self-Normalizing Neural Network (SS-NN) that leverages internal EDFA features - such as VOA input or output power and attenuation, to improve gain spectrum prediction. Our SS-NN model employs a…

Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning
Xiaoran Xua, In-Ho Rab, Ravi Sankarc
https://arxiv.org/abs/2507.16845 https://arxiv.o…

Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learning
Lung diseases, including lung cancer and COPD, are significant health concerns globally. Traditional diagnostic methods can be costly, time-consuming, and invasive. This study investigates the use of semi supervised learning methods for lung sound signal detection using a model combination of MFCC+CNN. By introducing semi supervised learning modules such as Mix Match, Co-Refinement, and Co Refurbishing, we aim to enhance the detection performance while reducing dependence on manual annotations.…

Adversarial Graph Fusion for Incomplete Multi-view Semi-supervised Learning with Tensorial Imputation
Zhangqi Jiang, Tingjin Luo, Xu Yang, Xinyan Liang
https://arxiv.org/abs/2509.15955

Adversarial Graph Fusion for Incomplete Multi-view Semi-supervised Learning with Tensorial Imputation
View missing remains a significant challenge in graph-based multi-view semi-supervised learning, hindering their real-world applications. To address this issue, traditional methods introduce a missing indicator matrix and focus on mining partial structure among existing samples in each view for label propagation (LP). However, we argue that these disregarded missing samples sometimes induce discontinuous local structures, i.e., sub-clusters, breaking the fundamental smoothness assumption in LP.…

A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys
Yufeng Luo, Adam D. Myers, Alex Drlica-Wagner, Dario Dematties, Salma Borchani, Frank Valdes, Arjun Dey, David Schlegel, Rongpu Zhou, DESI Legacy Imaging Surveys Team
https://arxiv.org/abs/2507.12784

A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys
As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impractical. We introduce a machine-learning-based approach to detect poor-quality exposures in large imaging surveys, with a focus on the DECam Legacy Survey (DECaLS) in regions of low extinction (i.e., $E(B-V)<0.04$). Our semi-supervised pipeline integrates a vision transformer (ViT), trained via self-supervised learnin…

Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation
Haijian Ma, Daizong Liu, Xiaowen Cai, Pan Zhou, Yulai Xie
https://arxiv.org/abs/2508.18148 https:…

Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation
Intrusion Detection Systems (IDS) play a crucial role in network security defense. However, a significant challenge for IDS in training detection models is the shortage of adequately labeled malicious samples. To address these issues, this paper introduces a novel semi-supervised framework \textbf{GANGRL-LLM}, which integrates Generative Adversarial Networks (GANs) with Large Language Models (LLMs) to enhance malicious code generation and SQL Injection (SQLi) detection capabilities in few-sampl…

nnFilterMatch: A Unified Semi-Supervised Learning Framework with Uncertainty-Aware Pseudo-Label Filtering for Efficient Medical Segmentation
Yi Yang
https://arxiv.org/abs/2509.19746

nnFilterMatch: A Unified Semi-Supervised Learning Framework with Uncertainty-Aware Pseudo-Label Filtering for Efficient Medical Segmentation
Semi-supervised learning (SSL) has emerged as a promising paradigm in medical image segmentation, offering competitive performance while substantially reducing the need for extensive manual annotation. When combined with active learning (AL), these strategies further minimize annotation burden by selectively incorporating the most informative samples. However, conventional SSL_AL hybrid approaches often rely on iterative and loop-based retraining cycles after each annotation round, incurring si…

TACTFL: Temporal Contrastive Training for Multi-modal Federated Learning with Similarity-guided Model Aggregation
Guanxiong Sun, Majid Mirmehdi, Zahraa Abdallah, Raul Santos-Rodriguez, Ian Craddock, Telmo de Menezes e Silva Filho
https://arxiv.org/abs/2509.17532

TACTFL: Temporal Contrastive Training for Multi-modal Federated Learning with Similarity-guided Model Aggregation
Real-world federated learning faces two key challenges: limited access to labelled data and the presence of heterogeneous multi-modal inputs. This paper proposes TACTFL, a unified framework for semi-supervised multi-modal federated learning. TACTFL introduces a modality-agnostic temporal contrastive training scheme that conducts representation learning from unlabelled client data by leveraging temporal alignment across modalities. However, as clients perform self-supervised training on heteroge…

Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation
Guangyu Sun, Jingtao Li, Weiming Zhuang, Chen Chen, Chen Chen, Lingjuan Lyu
https://arxiv.org/abs/2508.16568

Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation
Foundation models (FMs) exhibit remarkable generalization but require adaptation to downstream tasks, particularly in privacy-sensitive applications. Due to data privacy regulations, cloud-based FMs cannot directly access private edge data, limiting their adaptation. Federated learning (FL) provides a privacy-aware alternative, but existing FL approaches overlook the constraints imposed by edge devices -- namely, limited computational resources and the scarcity of labeled data. To address these…

SSSUMO: Real-Time Semi-Supervised Submovement Decomposition
Evgenii Rudakov, Jonathan Shock, Otto Lappi, Benjamin Ultan Cowley
https://arxiv.org/abs/2507.08028

SSSUMO: Real-Time Semi-Supervised Submovement Decomposition
This paper introduces a SSSUMO, semi-supervised deep learning approach for submovement decomposition that achieves state-of-the-art accuracy and speed. While submovement analysis offers valuable insights into motor control, existing methods struggle with reconstruction accuracy, computational cost, and validation, due to the difficulty of obtaining hand-labeled data. We address these challenges using a semi-supervised learning framework. This framework learns from synthetic data, initially gene…

Semi-Supervised Learning with Online Knowledge Distillation for Skin Lesion Classification
Siyamalan Manivannan
https://arxiv.org/abs/2508.11511 https://ar…

Semi-Supervised Learning with Online Knowledge Distillation for Skin Lesion Classification
Deep Learning has emerged as a promising approach for skin lesion analysis. However, existing methods mostly rely on fully supervised learning, requiring extensive labeled data, which is challenging and costly to obtain. To alleviate this annotation burden, this study introduces a novel semi-supervised deep learning approach that integrates ensemble learning with online knowledge distillation for enhanced skin lesion classification. Our methodology involves training an ensemble of convolutional…

Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation
Nguyen Lan Vi Vu, Thanh-Huy Nguyen, Thien Nguyen, Daisuke Kihara, Tianyang Wang, Xingjian Li, Min Xu
https://arxiv.org/abs/2509.13834

Semi-MoE: Mixture-of-Experts meets Semi-Supervised Histopathology Segmentation
Semi-supervised learning has been employed to alleviate the need for extensive labeled data for histopathology image segmentation, but existing methods struggle with noisy pseudo-labels due to ambiguous gland boundaries and morphological misclassification. This paper introduces Semi-MOE, to the best of our knowledge, the first multi-task Mixture-of-Experts framework for semi-supervised histopathology image segmentation. Our approach leverages three specialized expert networks: A main segmentati…

End-to-end Acoustic-linguistic Emotion and Intent Recognition Enhanced by Semi-supervised Learning
Zhao Ren, Rathi Adarshi Rammohan, Kevin Scheck, Sheng Li, Tanja Schultz
https://arxiv.org/abs/2507.07806

End-to-end Acoustic-linguistic Emotion and Intent Recognition Enhanced by Semi-supervised Learning
Emotion and intent recognition from speech is essential and has been widely investigated in human-computer interaction. The rapid development of social media platforms, chatbots, and other technologies has led to a large volume of speech data streaming from users. Nevertheless, annotating such data manually is expensive, making it challenging to train machine learning models for recognition purposes. To this end, we propose applying semi-supervised learning to incorporate a large scale of unlab…

Fast and Simple Multiclass Data Segmentation: An Eigendecomposition and Projection-Free Approach
Chiara Faccio, Margherita Porcelli, Francesco Rinaldi, Martin Stoll
https://arxiv.org/abs/2508.09738

Fast and Simple Multiclass Data Segmentation: An Eigendecomposition and Projection-Free Approach
Graph-based machine learning has seen an increased interest over the last decade with many connections to other fields of applied mathematics. Learning based on partial differential equations, such as the phase-field Allen-Cahn equation, allows efficient handling of semi-supervised learning approaches on graphs. The numerical solution of the graph Allen-Cahn equation via a convexity splitting or the Merriman-Bence-Osher (MBO) scheme, albeit being a widely used approach, requires the calculation…

Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation
Mohammad R. Salmanpour, Amir Hossein Pouria, Sonia Falahati, Shahram Taeb, Somayeh Sadat Mehrnia, Ali Fathi Jouzdani, Mehrdad Oveisi, Ilker Hacihaliloglu, Arman Rahmim
https://arxiv.org/abs/2507.0818…

Robust Semi-Supervised CT Radiomics for Lung Cancer Prognosis: Cost-Effective Learning with Limited Labels and SHAP Interpretation
Background: CT imaging is vital for lung cancer management, offering detailed visualization for AI-based prognosis. However, supervised learning SL models require large labeled datasets, limiting their real-world application in settings with scarce annotations. Methods: We analyzed CT scans from 977 patients across 12 datasets extracting 1218 radiomics features using Laplacian of Gaussian and wavelet filters via PyRadiomics Dimensionality reduction was applied with 56 feature selection and ex…

U-Mamba2-SSL for Semi-Supervised Tooth and Pulp Segmentation in CBCT
Zhi Qin Tan, Xiatian Zhu, Owen Addison, Yunpeng Li
https://arxiv.org/abs/2509.20154 https://

U-Mamba2-SSL for Semi-Supervised Tooth and Pulp Segmentation in CBCT
Accurate segmentation of teeth and pulp in Cone-Beam Computed Tomography (CBCT) is vital for clinical applications like treatment planning and diagnosis. However, this process requires extensive expertise and is exceptionally time-consuming, highlighting the critical need for automated algorithms that can effectively utilize unlabeled data. In this paper, we propose U-Mamba2-SSL, a novel semi-supervised learning framework that builds on the U-Mamba2 model and employs a multi-stage training stra…

Encoder-Inverter Framework for Seismic Acoustic Impedance Inversion
Junheng Peng, Yingtian Liu, Mingwei Wang, Yong Li, Wen Feng
https://arxiv.org/abs/2507.19933 https://

Encoder-Inverter Framework for Seismic Acoustic Impedance Inversion
Seismic acoustic impedance inversion is one of the most challenging tasks in geophysical exploration, facing difficulties such as insufficient well-logging data and non-linear relationships. Most inversion methods rely on strategies such as semi-supervised learning, and their inversion accuracy and stability still need further improvement. In this work, we propose a novel inversion framework that innovatively maps continuous seismic traces into high-dimensional linear features, transforming the…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/9]:
- Simple yet Effective Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-...
Seongjae Kang, Dong Bok Lee, Hyungjoon Jang, Sung Ju Hwang

Contrastive-KAN: A Semi-Supervised Intrusion Detection Framework for Cybersecurity with scarce Labeled Data
Mohammad Alikhani, Reza Kazemi
https://arxiv.org/abs/2507.10808

Contrastive-KAN: A Semi-Supervised Intrusion Detection Framework for Cybersecurity with scarce Labeled Data
In the era of the Fourth Industrial Revolution, cybersecurity and intrusion detection systems are vital for the secure and reliable operation of IoT and IIoT environments. A key challenge in this domain is the scarcity of labeled cyber-attack data, as most industrial systems operate under normal conditions. This data imbalance, combined with the high cost of annotation, hinders the effective training of machine learning models. Moreover, rapid detection of attacks is essential, especially in cr…

Data augmentation enables label-specific generation of homologous protein sequences
Lorenzo Rosset, Martin Weigt, Francesco Zamponi
https://arxiv.org/abs/2507.15651

Data augmentation enables label-specific generation of homologous protein sequences
Accurately annotating and controlling protein function from sequence data remains a major challenge, particularly within homologous families where annotated sequences are scarce and structural variation is minimal. We present a two-stage approach for semi-supervised functional annotation and conditional sequence generation in protein families using representation learning. First, we demonstrate that protein language models, pretrained on large and diverse sequence datasets and possibly finetune…

Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semi-Supervised Deep Learning
Jianzhen Chen, Zhijian Luo, Cheng Cheng, Jun Hou, Shaohua Zhang, Chenggang Shu
https://arxiv.org/abs/2507.07552

Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semi-Supervised Deep Learning
The ring structures of disk galaxies are vital for understanding galaxy evolution and dynamics. However, due to the scarcity of ringed galaxies and challenges in their identification, traditional methods often struggle to efficiently obtain statistically significant samples. To address this, this study employs a novel semi-supervised deep learning model, GC-SWGAN, aimed at identifying galaxy rings from high-resolution images of the DESI Legacy Imaging Surveys. We selected over 5,000 confirmed r…

Enhancing Dual Network Based Semi-Supervised Medical Image Segmentation with Uncertainty-Guided Pseudo-Labeling
Yunyao Lu, Yihang Wu, Ahmad Chaddad, Tareef Daqqaq, Reem Kateb
https://arxiv.org/abs/2509.13084

Enhancing Dual Network Based Semi-Supervised Medical Image Segmentation with Uncertainty-Guided Pseudo-Labeling
Despite the remarkable performance of supervised medical image segmentation models, relying on a large amount of labeled data is impractical in real-world situations. Semi-supervised learning approaches aim to alleviate this challenge using unlabeled data through pseudo-label generation. Yet, existing semi-supervised segmentation methods still suffer from noisy pseudo-labels and insufficient supervision within the feature space. To solve these challenges, this paper proposes a novel semi-superv…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/8]:
- Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning
Jungtaek Kim

Efficient Learning for Product Attributes with Compact Multimodal Models
Mandar Kulkarni
https://arxiv.org/abs/2507.19679 https://arxiv.org/pdf/2507.19679

Efficient Learning for Product Attributes with Compact Multimodal Models
Image-based product attribute prediction in e-commerce is a crucial task with numerous applications. The supervised fine-tuning of Vision Language Models (VLMs) faces significant scale challenges due to the cost of manual or API based annotation. In this paper, we investigate label-efficient semi-supervised fine-tuning strategies for compact VLMs (2B-3B parameters) that leverage unlabeled product listings through Direct Preference Optimization (DPO). Beginning with a small, API-based, annotated…

Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning
Jin Yang, Daniel S. Marcus, Aristeidis Sotiras
https://arxiv.org/abs/2509.10784

Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning
Medical Vision Foundation Models (Med-VFMs) have superior capabilities of interpreting medical images due to the knowledge learned from self-supervised pre-training with extensive unannotated images. To improve their performance on adaptive downstream evaluations, especially segmentation, a few samples from target domains are selected randomly for fine-tuning them. However, there lacks works to explore the way of adapting Med-VFMs to achieve the optimal performance on target domains efficiently…

SPARSE Data, Rich Results: Few-Shot Semi-Supervised Learning via Class-Conditioned Image Translation
Guido Manni, Clemente Lauretti, Loredana Zollo, Paolo Soda
https://arxiv.org/abs/2508.06429

SPARSE Data, Rich Results: Few-Shot Semi-Supervised Learning via Class-Conditioned Image Translation
Deep learning has revolutionized medical imaging, but its effectiveness is severely limited by insufficient labeled training data. This paper introduces a novel GAN-based semi-supervised learning framework specifically designed for low labeled-data regimes, evaluated across settings with 5 to 50 labeled samples per class. Our approach integrates three specialized neural networks -- a generator for class-conditioned image translation, a discriminator for authenticity assessment and classificatio…

Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentation
Kaiwen Huang, Tao Zhou, Huazhu Fu, Yizhe Zhang, Yi Zhou, Xiao-Jun Wu
https://arxiv.org/abs/2508.09014

Uncertainty-aware Cross-training for Semi-supervised Medical Image Segmentation
Semi-supervised learning has gained considerable popularity in medical image segmentation tasks due to its capability to reduce reliance on expert-examined annotations. Several mean-teacher (MT) based semi-supervised methods utilize consistency regularization to effectively leverage valuable information from unlabeled data. However, these methods often heavily rely on the student model and overlook the potential impact of cognitive biases within the model. Furthermore, some methods employ co-tr…

Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Haoran Xi, Chen Liu, Xiaolin Li
https://arxiv.org/abs/2508.06517

Frequency Prior Guided Matching: A Data Augmentation Approach for Generalizable Semi-Supervised Polyp Segmentation
Automated polyp segmentation is essential for early diagnosis of colorectal cancer, yet developing robust models remains challenging due to limited annotated data and significant performance degradation under domain shift. Although semi-supervised learning (SSL) reduces annotation requirements, existing methods rely on generic augmentations that ignore polyp-specific structural properties, resulting in poor generalization to new imaging centers and devices. To address this, we introduce Frequen…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- A Computational Theory and Semi-Supervised Algorithm for Clustering
Nassir Mohammad

ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholds
Shanle Yao, Ghazal Alinezhad Noghre, Armin Danesh Pazho, Hamed Tabkhi
https://arxiv.org/abs/2508.09058

ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholds
Video Anomaly Detection (VAD) can play a key role in spotting unusual activities in video footage. VAD is difficult to use in real-world settings due to the dynamic nature of human actions, environmental variations, and domain shifts. Traditional evaluation metrics often prove inadequate for such scenarios, as they rely on static assumptions and fall short of identifying a threshold that distinguishes normal from anomalous behavior in dynamic settings. To address this, we introduce an active le…

When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective
Lin-Han Jia, Si-Yu Han, Wen-Chao Hu, Jie-Jing Shao, Wen-Da Wei, Zhi Zhou, Lan-Zhe Guo, Yu-Feng Li
https://arxiv.org/abs/2508.07299

When Is Prior Knowledge Helpful? Exploring the Evaluation and Selection of Unsupervised Pretext Tasks from a Neuro-Symbolic Perspective
Neuro-symbolic (Nesy) learning improves the target task performance of models by enabling them to satisfy knowledge, while semi/self-supervised learning (SSL) improves the target task performance by designing unsupervised pretext tasks for unlabeled data to make models satisfy corresponding assumptions. We extend the Nesy theory based on reliable knowledge to the scenario of unreliable knowledge (i.e., assumptions), thereby unifying the theoretical frameworks of SSL and Nesy. Through rigorous t…