Latest Self-Supervised Learning Research Papers

The newest Self-Supervised Learning papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Self-Supervised Learning so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• Contrastive Learning for Phishing Detection in Text-Based Environments
  Thomas Mulaisho, Emmanuel Michael, Sanket Kanekar, Qiunan Zhang et al. · Journal of the Association ... · Aug 15, 2026

  Phishing remains one of the most prevalent and effective forms of social engineering attacks in today’s online environment. Attackers typically impersonate trusted individuals or organizations to deceive users and gain access to sensitive i…

• Low Dose and High Contrast Biomedical Imaging Using SelfSupervised Deep Learning
  Xiao Fan Ding, Xiaoman Duan, Ning Zhu · Zenodo (CERN European Organ... · Aug 11, 2026

  Self-supervised deep learning has emerged as a powerful method for image enhancement when a priori ground-truth references are not available. Stemming from Noise2Noise , it was shown that a convolutional neural network (CNN) can be trained …

• Low Dose and High Contrast Biomedical Imaging Using SelfSupervised Deep Learning
  Xiao Fan Ding, Xiaoman Duan, Ning Zhu · Zenodo (CERN European Organ... · Aug 11, 2026

  Self-supervised deep learning has emerged as a powerful method for image enhancement when a priori ground-truth references are not available. Stemming from Noise2Noise , it was shown that a convolutional neural network (CNN) can be trained …

• Self-supervision drives representational convergence in medical foundation models more than clinical supervision
  Soroosh Tayebi Arasteh, Sebastian Ziegelmayer, Mahshad Lotfinia, Lisa Adams et al. · arXiv · Jul 22, 2026

  Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure. Whether this convergence is real, wha…

• User-Centric Modeling of Transactional Sequences with Explainable State Space Models
  Ivan Palagin · arXiv · Jul 22, 2026

  We propose a hybrid approach for user-centric modeling of transactional event sequences that combines contrastive representation learning (CoLES) with State Space Models (SSMs). While contrastive methods yield high-quality compressed user r…

• CircuitKIT : Circuit Discovery, Evaluation, and Application Toolkit for Mechanistic Interpretability
  Pratinav Seth, Hem Gosalia, Aditya Kasliwal, Vinay Kumar Sankarapu · arXiv · Jul 21, 2026

  Circuit analysis can support not only model explanation but also downstream interventions such as pruning, editing, steering, and selective fine-tuning. However, conducting such analyses currently requires stitching together separate implem…

• Contrastive-Collapsed Loss for Flexible and Geometrically Optimal Embeddings and Faster Convergence
  Blanca Cano-Camarero, Ángela Fernández-Pascual, José R. Dorronsoro · arXiv · Jul 14, 2026

  In this work, we introduce CoCo, a loss function aimed at learning normalized and well-structured representations. The proposed loss encourages intra-class collapse and inter-class contrast while preserving sufficient flexibility for neural…

• CatRetriever: Contrastive Representation Learning for Slab-to-Bulk Retrieval in Generative Catalyst Discovery
  Jungho Oh, Woosung Kim, Dong Hyeon Mok, Jonggeol Na et al. · arXiv · Jul 13, 2026

  Inverse design is an emerging data-driven paradigm for efficiently navigating vast chemical spaces to discover new materials with targeted properties, and in the context of heterogeneous catalysis, surface generative models have recently ad…

• CoCoT-EEG: Contrastive-Pretrained Multiscale Convolutional Transformer for EEG Decoding
  Gabriel Mahuas, Victoria Shevchenko, Ugo Tanielian, Yassir Bendou et al. · arXiv · Jul 10, 2026

  Self-supervised pretrained foundation models (FM) have shown early promise for non-invasive electroencephalogram (EEG) decoding applications. Many recent large-scale models converged on the approach of tokenizing raw EEG followed by masked …

• TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios
  Hong Lyu, Mingru Yang, Qianhua He, Yanxiong Li et al. · arXiv · Jul 7, 2026

  There are some datasets of varying scales for audio classification (AC) applied to different tasks. However, annotated data is limited for most scenarios, such as domestic environments. To address this challenge, we propose an $\textbf{A}$u…

• Understanding the Robustness of Distributed Self-Supervised Learning Frameworks Against Non-IID Data
  Xuanyu Chen, Nan Yang, Shuai Wang, Dong Yuan · arXiv · Jul 2, 2026

  Recent research has introduced distributed self-supervised learning (D-SSL) approaches to leverage vast amounts of unlabeled decentralized data. However, D-SSL faces the critical challenge of data heterogeneity, and there is limited theoret…

• Object-centric LeJEPA
  Jakob Geusen, Ender Konukoglu · arXiv · Jul 2, 2026

  Image encoders trained with LeJEPA can deliver strong features for downstream tasks, but, like other image-level self-supervised methods, typically require large training datasets. Aligning representations at the level of objects rather tha…

• A Lightweight Self-Supervised Learning Framework for Multivariate Time Series using Hierarchical-JEPA on ECG Data
  Siwon Kim · arXiv · Jul 1, 2026

  Data analysis in the medical domain often encounters scenarios involving a limited target dataset and a large, unannotated dataset with a general distribution. Under such circumstances, self-supervised learning (SSL) methods are highly effe…

• Optimization Dynamics Imprint Semantic Specificity in Contrastive Embedding Norms
  Ziwei Su, Junyu Ren, Victor Veitch · arXiv · Jun 29, 2026

  Contrastive embedding models trained with scale-invariant losses are typically paired with distance metrics like cosine similarity, effectively ignoring embedding magnitudes. However, surprisingly, empirical studies reveal that despite this…

• Hedgementation = Hedgerow Segmentation: A Remote Sensing Benchmark
  Nathan Senyard, Salem Hamdani, Astrid Zhang, Derek Wang et al. · arXiv · Jun 22, 2026

  We propose Hedgementation: a new benchmark to evaluate machine learning models for hedgerow mapping from remote sensing data at country scale and 10m$^2$ spatial resolution. We combine and harmonize multiple remote sensing data products and…

• Scaling Linear Mode Connectivity and Merging to Billion Parameter Pretrained Transformers
  Tianyi Li, Zhiqiang Shen · arXiv · Jun 22, 2026

  Linear mode connectivity (LMC) provides a promising foundation for understanding and merging independently trained neural networks, but existing methods typically optimize the interpolation path from only one model endpoint, limiting their …

• Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding Tasks
  Mengyu Zheng, Kai Han, Boxun Li, Haiyang Xu et al. · arXiv · Jun 10, 2026

  General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-bench: a generic agent does not by itself satisfy the clean Docker workspace, patch, and pred…

• OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib
  Abhijoy Sarkar, Aarchi Singh Thakur · arXiv · Jun 9, 2026

  Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computatio…

• Perturbative Contrastive Physical Learning
  Kyungeun Kim, Amanuel Anteneh, Israel Klich, Olivier Pfister et al. · arXiv · Jun 8, 2026

  Responses to perturbations are key to understanding physical systems. The ability to contrast such responses by comparing how a system reacts under slightly different conditions provides a mechanism for learning. Here, we introduce Perturba…

• Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles
  Xiao Li, Yixuan Jia, Zekai Zhang, Xiang Li et al. · arXiv · Jun 8, 2026

  Diffusion models have demonstrated remarkable generative capabilities and have also emerged as powerful self-supervised representation learners, yet the connection between these two abilities remains less explored. Drawing inspiration from …

• Beyond Binary: Speech Representations Across the Cognitive Score Hierarchy
  Serli Kopar, Roshan Prakash Rane, Christian Mychajliw, Lydia Federmann et al. · arXiv · May 26, 2026

  This study examines the relationship between speech representations and the hierarchical structure of cognitive assessment in mild cognitive impairment. Utilizing 5,754 German neuropsychological assessment recordings, we evaluate six cognit…

• FoundObj: Self-supervised Foundation Models as Rewards for Label-free 3D Object Segmentation
  Zihui Zhang, Zhixuan Sun, Yafei Yang, Jinxi Li et al. · arXiv · May 26, 2026

  We address the challenging task of 3D object segmentation in complex scene point clouds without relying on any scene-level human annotations during training. Existing methods are typically constrained to identifying simple objects, primaril…

• Self-Supervised Learning as Discrete Communication
  Kawtar Zaher, Ilyass Moummad, Olivier Buisson, Alexis Joly · ICML 2026 regular · Apr 30, 2026

  Most self-supervised learning (SSL) methods learn continuous visual representations by aligning different views of the same input, offering limited control over how information is structured across representation dimensions. In this work, w…

• On the Alignment Between Supervised and Self-Supervised Contrastive Learning
  Achleshwar Luthra, Priyadarsi Mishra, Tomer Galanti · ICLR 2026 Poster · Jan 26, 2026

  Self-supervised contrastive learning (CL) has achieved remarkable empirical success, often producing representations that rival supervised pre-training on downstream tasks. Recent theory explains this by showing that the CL loss closely app…

• LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
  Randall Balestriero, Yann LeCun · arXiv.org · Nov 11, 2025

  Learning manipulable representations of the world and its dynamics is central to AI. Joint-Embedding Predictive Architectures (JEPAs) offer a promising blueprint, but lack of practical guidance and theory has led to ad-hoc R&D. We present a…

• Concerto: Joint 2D-3D Self-Supervised Learning Emerges Spatial Representations
  Yujia Zhang, Xiaoyang Wu, Yixing Lao, Chengyao Wang et al. · arXiv.org · Oct 27, 2025

  Humans learn abstract concepts through multisensory synergy, and once formed, such representations can often be recalled from a single modality. Inspired by this principle, we introduce Concerto, a minimalist simulation of human concept lea…

• Self-Supervised Learning for Financial Statement Fraud Detection with Limited and Imbalanced Data
  Jianlin Lai, Anzhuo Xie, Hanrui Feng, Yi Wang et al. · Proceedings of the 4th International Conference on Artificial Intelligence and Intelligent Information Processing · Oct 24, 2025

  This study addresses the challenges of scarce fraudulent samples, complex data distributions, and the limited adaptability of traditional methods in financial statement fraud detection by proposing a self-supervised learning algorithm. The …

• SSNet: Flexible and Robust Channel Extrapolation for Fluid Antenna Systems Enabled by a Self-Supervised Learning Framework
  Yuan Gao, Yiming Liu, Runze Yu, Shengli Liu et al. · IEEE Journal on Selected Areas in Communications · Sep 22, 2025

  Fluid antenna systems (FAS) signify a pivotal advancement in 6G communication by enhancing spectral efficiency and robustness. However, obtaining accurate channel state information (CSI) in FAS poses challenges due to its complex physical s…

• Self-supervised learning in drug discovery
  Yangyang Chen, Zixu Wang, Jianmin Wang, Yanyi Chu et al. · Science China Information Sciences · Jun 23, 2025
• Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS
  Roman Bushuiev, Anton Bushuiev, Raman Samusevich, Corinna Brungs et al. · Nature Biotechnology · May 23, 2025

  Characterizing biological and environmental samples at a molecular level primarily uses tandem mass spectroscopy (MS/MS), yet the interpretation of tandem mass spectra from untargeted metabolomics experiments remains a challenge. Existing c…