Contrastive Learning

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Contrastive learning teaches a model which examples should have similar representations and which should be distinct, often using different views of the same item as positive pairs. Common approaches include InfoNCE methods such as SimCLR and MoCo, supervised and prototype-based losses, hard-negative mining, and CLIP-style alignment across modalities. These representations support classification, retrieval, and transfer when labels are scarce, across images, language, time series, graphs, and scientific data. A central challenge is choosing positives and negatives that capture useful invariances without treating semantically related examples as negatives or discarding information needed for downstream tasks; theory also studies representation collapse, geometry, and generalization.

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322 papers37 papers in the last four weeks, up 3% on the four weeks before. 0.2% of all new papers.

Contrastive Learning under Noisy Temporal Self-Supervision for Colonoscopy Videos
May 12, 2026Luca Parolari, Pietro Gori, Lamberto Ballan +2Contrastive Learning Endoscopy
Department of Mathematics, University of Padova, Padova, Italy · LTCI, Telecom Paris, Institut Polytechnique de Paris, Palaiseau, France · Cosmo Intelligent Medical Devices, Dublin, Ireland
Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification
May 12, 2026Chenxu Wang, Shuang Wang, Lirong Han +4Contrastive Learning Self-Supervised Learning
School of Artificial Intelligence, Xidian University, Xi’an 710071, China · Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China · School of Electronics And Information, Northwestern Polytechnical University, Xi’an 710072, China
Optimal Representations for Generalized Contrastive Learning with Imbalanced Datasets
May 11, 2026Thuan Nguyen, Shuchin Aeron, D. Richard Brown +1Class-Imbalanced Learning Contrastive Learning
Department of Engineering, Engineering Technology East Tennessee State University · Department of Electrical and Computer Engineering Tufts University · Department of Electrical and Computer Engineering Worcester Polytechnic Institute +1
Physical probes expose and alleviate chemical-environment collapse in molecular representations
May 11, 2026Jiebin Fang, Zidi Yan, Churu Mao +7Contrastive Learning Cross-Modal Representation Learning
Hainan Institute, Zhejiang University, Sanya 572025, China · Institute of Marine Biology and Pharmacology, Ocean College, Zhejiang University, Zhoushan 316021, China · School of Food and Pharmacy, Zhejiang Ocean University, Zhoushan 316021, China +2
Improving Lexical Difficulty Prediction with Context-Aligned Contrastive Learning and Ridge Ensembling
May 9, 2026Wicaksono Leksono Muhamad, Joanito Agili Lopo, Tsamarah Rana Nugraha +2Contrastive Learning Cross-Lingual Transfer
Mantera Studio · The University of Manchester · Universitas Gadjah Mada
SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization
May 9, 2026Yan Sun, Guoxia Wang, Jinle Zeng +6Language Model Pretraining Contrastive Learning
Baidu Inc. · Nanyang Technological University
MoMo: Conditioned Contrastive Representation Learning for Preference-Modulated Planning
May 8, 2026Yusuf Syed, Viraj Parimi, Brian WilliamsContrastive Learning Risk-Sensitive Planning
Massachusetts Institute of Technology
A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning
May 8, 2026Nong Minh Hieu, Antoine LedentClass-Imbalanced Learning Contrastive Learning
School of Computing and Information Systems, Singapore Management University.
ProtSent: Protein Sentence Transformers
May 7, 2026Dan Ofer, Oriel Perets, Michal Linial +1Contrastive Learning Protein Language Models
Department of Biological Chemistry The Hebrew University of Jerusalem · Department of Computer and Information Science Ben-Gurion University of the Negev
DINORANKCLIP: DINOv3 Distillation and Injection for Vision-Language Pretraining with High-Order Ranking Consistency
May 7, 2026Shuyang Jiang, Nan Yu, Yiming Zhang +2Contrastive Learning Vision-Language Alignment
University of California, Los Angeles · Aimaikj · HFIPS, Chinese Academy of Sciences +2
CoMemNet: Contrastive Sampling with Memory Replay Network for Continual Traffic Prediction
May 7, 2026Mei Wu, Wenchao Weng, Wenxin Su +2Contrastive Learning Traffic Flow Estimation
College of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China · College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China · School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Chaotic Contrastive Learning for Robust Texture Classification
May 6, 2026Joao B FlorindoContrastive Learning Synthetic Data Augmentation
Institute of Mathematics, Statistics and Scientific Computing of the University of Campinas, Rua Sergio Buarque de Holanda, 651, Campinas, 13083-859, Sao Paulo, Brazil
Interpreting V1 Population Activity via Image-Neural Latent Representation Alignment
May 5, 2026Xin Wang, Zhuangzhi Gao, Hongyi Qin +3Contrastive Learning Neural Processes
School of Psychological and Cognitive Sciences, Peking University, Beijing, China · Department of Primary Care and Mental Health, University of Liverpool, Liverpool, United Kingdom · Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, United Kingdom +2
Memory-Efficient Continual Learning with CLIP Models
May 5, 2026Ryan King, Gang Li, Bobak Mortazavi +1Class-Incremental Learning Contrastive Learning
Department of Computer Science Texas A&M University College Station, TX 77843 · Texas A&M University College Station, TX 77843
Provable Accuracy Collapse in Embedding-Based Representations under Dimensionality Mismatch
May 5, 2026Dionysis Arvanitakis, Vaggos Chatziafratis, Yiyuan LuoIntrinsic Dimensionality Contrastive Learning
Northwestern University, IL, USA · University of California
Statistical Consistency and Generalization of Contrastive Representation Learning
May 4, 2026Yuanfan Li, Xiyuan Wei, Tianbao Yang +1Contrastive Learning Supervised Contrastive Learning
University of Sydney · 2Texas A&M University
Repurposing and Evaluating the (In)Feasibility of Dataset Poisoning enabled Watermarking for Contrastive Learning
May 3, 2026Zhiyang Dai, Yansong Gao, Boyu Kuang +5Contrastive Learning Dataset Watermarking
School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China · University of Western Australia, Perth, Australia · College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China +3
CEZSAR: A Contrastive Embedding Method for Zero-Shot Action Recognition
May 1, 2026Valter Estevam, Rayson Laroca, Helio Pedrini +1Contrastive Learning Zero-Shot Learning
Federal Institute of Paraná, Irati, Brasil · Federal University of Paraná, Curitiba, Brasil · Pontifical Catholic University of Paraná, Curitiba, Brasil +1
UHR-Net: An Uncertainty-Aware Hypergraph Refinement Network for Medical Image Segmentation
Apr 30, 2026Shuokun Cheng, Jinghao Shi, Kun SunContrastive Learning Image Segmentation
School of Computer Sciences, China University of Geosciences (Wuhan), Wuhan 430074, Hubei, China
ZAYAN: Disentangled Contrastive Transformer for Tabular Remote Sensing Data
Apr 30, 2026Al Zadid Sultan Bin Habib, Tanpia Tasnim, Md. Ekramul Islam +1Contrastive Learning Disentangled Representation Learning
Lane Dept. of Computer Science and Electrical Engineering* and Dept. of Geology & Geography+, West Virginia University, Morgantown, WV 26506, USA · Department of Computer Science and Engineering, Green University of Bangladesh, Narayanganj-1461, Bangladesh · Department of Computer Science and Engineering, Stamford University Bangladesh, Dhaka-1217, Bangladesh
SparseContrast: Dynamic Sparse Attention for Efficient and Accurate Contrastive Learning in Medical Imaging
Apr 27, 2026Paarth Prasad, Ruchika MalhotraContrastive Learning Chest X-Ray Classification
Department of Software Engineering, Delhi Technological University
Selective Contrastive Learning For Gloss Free Sign Language Translation
Apr 24, 2026Changhao Lai, Rui Zhao, Xuewen Zhong +2Contrastive Learning Sign Language Translation
School of Informatics, Xiamen University, China · 2Key Lab of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian-Taiwan (XMU), Ministry of Culture and Tourism, China · 3National Language Resources Monitoring and Research Center for Education and Teaching Media, Xiamen University, China
Trust-SSL: Additive-Residual Selective Invariance for Robust Aerial Self-Supervised Learning
Apr 23, 2026Wadii Boulila, Adel Ammar, Bilel Benjdira +1Contrastive Learning Image Corruption Robustness
Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 11586, Saudi Arabia
Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis
Apr 22, 2026Junyu Ren, Wensheng Gan, Philip S YuContrastive Learning Cross-Domain Generalization
College of Cyber Security, Jinan University, Guangzhou 510632, China · Department of Computer Science, University of Illinois Chicago, Chicago, USA
Structure-guided molecular design with contrastive 3D protein-ligand learning
Apr 21, 2026Carles Navarro, Philipp Tholke, Gianni de FabritiisContrastive Learning Virtual Screening
Acellera Labs, C/ Doctor Trueta 183, 08005 Barcelona, Spain · Computational Science Laboratory, Universitat Pompeu Fabra, PRBB, C/ Doctor Aiguader 88, 08003 Barcelona, Spain · Instituci´o Catalana de Recerca i Estudis Avan¸cats (ICREA), Passeig Llu´ıs Companys 23, 08010 Barcelona, Spain
Modeling Human Perspectives with Socio-Demographic Representations
Apr 20, 2026Leixin Zhang, Cagri ColtekinContrastive Learning Annotator Disagreement
University of Tübingen
Beyond Binary Contrast: Modeling Continuous Skeleton Action Spaces with Transitional Anchors
Apr 20, 2026Yingjie Feng, Yi Wang, Jiaze Wang +2Skeleton-Based Action Recognition Contrastive Learning
Harbin Institute of Technology, Shenzhen · Central South University · FitX Technology (Hong Kong) Limited
Hyperbolic Enhanced Representation Learning for Incomplete Multi-view Clustering
Apr 18, 2026Tianyi Chen, Haobo Wang, Kai Tang +5Contrastive Learning Hierarchical Representation Learning
School of Software Technology, Zhejiang University, Zhejiang 310027, China · College of Computer Science and Technology, Zhejiang University, Zhejiang 310027, China · Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China +2
OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction
Apr 18, 2026Zhongyuan Liang, Junhyung Jo, Hyang-Jung Lee +2Contrastive Learning Cross-Modal Knowledge Distillation
Computational Precision Health UC Berkeley, UCSF · Samsung Advanced Institute of Technology (SAIT) · Computational Precision Health UC Berkeley, UCSF[cs.LG]
UniCon: Unified Framework for Efficient Contrastive Alignment via Kernels
Apr 17, 2026Hangke Sui, Yuqing Wang, Minh N DoContrastive Learning Cross-Modal Alignment
Department of Electrical & Computer Engineering, The Grainger College of Engineering, UIUC · Coordinated Science Laboratory, University of Illinois Urbana-Champaign (UIUC) · Siebel School of Computing and Data Science, The Grainger College of Engineering, UIUC +1

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Contrastive Learning under Noisy Temporal Self-Supervision for Colonoscopy Videos

Luca Parolari, Pietro Gori, Lamberto Ballan, Carlo Biffi+1

Department of Mathematics, University of Padova, Padova, Italy · LTCI, Telecom Paris, Institut Polytechnique de Paris, Palaiseau, France · Cosmo Intelligent Medical Devices, Dublin, Ireland

Contrastive Learning Endoscopy Medical Image Analysis Video Representation Learning Noisy-Label Learning Medical Image Representation Learning

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Learning robust representations of polyp tracklets is key to enabling multiple AI-assisted colonoscopy applications, from polyp characterization to automated reporting and retrieval. Supervised contrastive learning is an effective approach for learning such representations, but it typically relies on correct positive and negative definitions. Collecting these labels requires linking tracklets that depict the same underlying polyp entity throughout the video, which is costly and demands specialized clinical expertise. In this work, we leverage the sequential workflow of colonoscopy procedures to derive self-supervised associations from temporal structure. Since temporally derived associations are not guaranteed to be correct, we introduce a noise-aware contrastive loss to account for noisy associations. We demonstrate the effectiveness of the learned representations across multiple downstream tasks, including polyp retrieval and re-identification, size estimation, and histology classification. Our method outperforms prior self-supervised and supervised baselines, and matches or exceeds recent foundation models across all tasks, using a lightweight encoder trained on only 27 videos. Code is available at https://github.com/lparolari/ntssl.