Awesome CONTRASTIVE LEARNING

A comprehensive list of awesome contrastive self-supervised learning papers.

PAPERS

Surveys and Reviews

• [ ] 2020: A Survey on Contrastive Self-Supervised Learning

2024

• [ ] 2024: Topic Modeling as Multi-Objective Contrastive Optimization
• [ ] 2024: Demonstrating and Reducing Shortcuts in Vision-Language Representation Learning [Code]
• [ ] 2024: VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis [Code]
• [ ] 2024: Multi-Grained Contrast for Data-Efficient Unsupervised Representation Learning [Code]
• [ ] 2024: Kdmcse: Knowledge distillation multimodal sentence embeddings with adaptive angular margin contrastive learning [Code]

2023

• [ ] 2023: Improving multimodal sentiment analysis: Supervised angular margin-based contrastive learning for enhanced fusion representation
• [ ] 2023: Inter-Instance Similarity Modeling for Contrastive Learning [Code]
• [ ] 2023: Asymmetric Patch Sampling for Contrastive Learning [Code]
• [ ] 2023: Randomized Schur Complement Views for Graph Contrastive Learning [Code]

2022

• [ ] 2022: Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions [Code]
• [ ] 2022: Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection
• [ ] 2022: Fair Contrastive Learning for Facial Attribute Classification (FSCL)

2021

• [ ] 2021: Contrastive Learning for Neural Topic Model [Code]
• [ ] 2021: Learning Transferable Visual Models From Natural Language Supervision (CLIP)
• [ ] 2021: Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images
• [ ] 2021: Robust Contrastive Learning Using Negative Samples with Diminished Semantics
• [ ] 2021: VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
• [ ] 2021: Barlow Twins: Self-Supervised Learning via Redundancy Reduction
• [ ] 2021: Poisoning and Backdooring Contrastive Learning
• [ ] 2021: Adversarial Attacks are Reversible with Natural Supervision
• [ ] 2021: Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels
• [ ] 2021: Understanding Cognitive Fatigue from fMRI Scans with Self-supervised Learning
• [ ] 2021: A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning
• [ ] 2021: Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation
• [ ] 2021: Contrastive Learning with Stronger Augmentations
• [ ] 2021: Dual Contrastive Learning for Unsupervised Image-to-Image Translation
• [ ] 2021: How Well Do Self-Supervised Models Transfer?
• [ ] 2021: Self-supervised Pretraining of Visual Features in the Wild
• [ ] 2021: VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples
• [ ] 2021: Temporal Contrastive Graph for Self-supervised Video Representation Learning
• [ ] 2021: Active Learning by Acquiring Contrastive Examples
• [ ] 2021: Active Contrastive Learning of Audio-Visual Video Representations

2020

• [ ] 2020: Rethinking the Value of Labels for Improving Class-Imbalanced Learning
• [ ] 2020: Online Bag-of-Visual-Words Generation for Unsupervised Representation Learning
• [ ] 2020: Social NCE: Contrastive Learning of Socially-aware Motion Representations
• [ ] 2020: CASTing Your Model: Learning to Localize Improves Self-Supervised Representations
• [ ] 2020: Exploring Simple Siamese Representation Learning
• [ ] 2020: FROST: Faster and more Robust One-shot Semi-supervised Training
• [ ] 2020: Hard Negative Mixing for Contrastive Learning
• [ ] 2020: Representation Learning via Invariant Causal Mechanisms
• [ ] 2020: Are all negatives created equal in contrastive instance discrimination?
• [ ] 2020: Bootstrap your own latent: A new approach to self-supervised Learning
• [ ] 2020: Spatiotemporal Contrastive Video Representation Learning
• [ ] 2020: Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition
• [ ] 2020: Deep Robust Clustering by Contrastive Learning
• [ ] 2020: Contrastive Learning for Unpaired Image-to-Image Translation
• [ ] 2020: Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases
• [ ] 2020: What Should Not Be Contrastive in Contrastive Learning
• [ ] 2020: Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework
• [ ] 2020: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
• [ ] 2020: Prototypical Contrastive Learning of Unsupervised Representations
• [ ] 2020: GraphCL: Contrastive Self-Supervised Learning of Graph Representations
• [ ] 2020: DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations
• [ ] 2020: Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models
• [ ] 2020: CERT: Contrastive Self-supervised Learning for Language Understanding
• [ ] 2020: Deep Graph Contrastive Representation Learning
• [ ] 2020: CLOCS: Contrastive Learning of Cardiac Signals
• [ ] 2020: On Mutual Information in Contrastive Learning for Visual Representations
• [ ] 2020: What makes for good views for contrastive learning
• [ ] 2020: CURL: Contrastive Unsupervised Representations for Reinforcement Learning
• [ ] 2020: Supervised Contrastive Learning
• [ ] 2020: Clustering based Contrastive Learning for Improving Face Representations
• [ ] 2020: A Simple Framework for Contrastive Learning of Visual Representations
• [ ] 2020: Improved Baselines with Momentum Contrastive Learning
• [ ] 2020: ALICE: Active Learning with Contrastive Natural Language Explanations

2019

• [ ] 2019: Unsupervised Scene Adaptation with Memory Regularization in vivo
• [ ] 2019: Self-labelling via simultaneous clustering and representation learning
• [ ] 2019: Transferable Contrastive Network for Generalized Zero-Shot Learning
• [ ] 2019: MoCo: Momentum Contrast for Unsupervised Visual Representation Learning
• [ ] 2019: Self-Supervised Learning of Pretext-Invariant Representations
• [ ] 2019: Selfie: Self-supervised Pretraining for Image Embedding
• [ ] 2019: Data-Efficient Image Recognition with Contrastive Predictive Coding
• [ ] 2019: Local Aggregation for Unsupervised Learning of Visual Embeddings
• [ ] 2019: Learning Representations by Maximizing Mutual Information Across Views
• [ ] 2019: Contrastive Multiview Coding
• [ ] 2019: Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
• [ ] 2019: Invariant Information Clustering for Unsupervised Image Classification and Segmentation
• [ ] 2019: A Theoretical Analysis of Contrastive Unsupervised Representation Learning

2018

• [ ] 2018: Learning deep representations by mutual information estimation and maximization
• [ ] 2018: Representation Learning with Contrastive Predictive Coding
• [ ] 2018: Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

2017 and Older

• [ ] 2017: Time-Contrastive Networks: Self-Supervised Learning from Video
• [ ] 2017: Multi-task Self-Supervised Visual Learning
• [ ] 2017: Unsupervised learning of visual representations by solving jigsawpuzzles
• [ ] 2015: Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
• [ ] 2010: Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

Star History