Contrastive Learning

Contrastive learning is a self-supervised machine learning technique that aims to learn robust data representations by contrasting similar and dissimilar data points. Current research focuses on applying contrastive learning to diverse modalities, including images, audio, text, and time-series data, often within multimodal frameworks and using architectures like MoCo and SimCLR, and exploring its application in various tasks such as object detection, speaker verification, and image dehazing. This approach is significant because it allows for effective learning from unlabeled or weakly labeled data, improving model generalization and performance across numerous applications, particularly in scenarios with limited annotated data or significant domain shifts.

Papers

June 9, 2023

DocumentCLIP: Linking Figures and Main Body Text in Reflowed Documents
Fuxiao Liu, Hao Tan, Chris Tensmeyer
Contrastive Learning Vision Language Multi Modal Document Relevance Full Text
Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression Values
Anchen Sun, Elizabeth J. Franzmann, Zhibin Chen, Xiaodong Cai
Contrastive Learning Gene Expression Data Cancer Genome Atlas Cancer Prognosis
Liquidity takers behavior representation through a contrastive learning approach
Ruihua Ruan, Emmanuel Bacry, Jean-François Muzy
Contrastive Learning Self Supervised K Mean Vector Representation

June 8, 2023

A brief review of contrastive learning applied to astrophysics
Marc Huertas-Company, Regina Sarmiento, Johan Knapen
Contrastive Learning Self Supervised Astronomical Data High Dimensional Space Brief Review Supervised Classification
Factorized Contrastive Learning: Going Beyond Multi-view Redundancy
Paul Pu Liang, Zihao Deng, Martin Ma, James Zou, Louis-Philippe Morency, Ruslan Salakhutdinov
Contrastive Learning Multimodal Task Multimodal Self Supervised Learning
Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce
Juan Gong, Zhenlin Chen, Chaoyi Ma, Zhuojian Xiao, Haonan Wang, Guoyu Tang, Lin Liu, Sulong Xu, Bo Long, Yunjiang Jiang
Contrastive Learning Expert Knowledge E Commerce Ranking Model User Representation Ranking Task Personalized Ranking Mixture of Attention
Contrastive Representation Disentanglement for Clustering
Fei Ding, Dan Zhang, Yin Yang, Venkat Krovi, Feng Luo
Contrastive Learning Self Supervised Learning Contrastive Loss COntrastive Disentanglement
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
Nan Yin, Li Shen, Mengzhu Wang, Long Lan, Zeyu Ma, Chong Chen, Xian-Sheng Hua, Xiao Luo
Contrastive Learning Graph Representation Graph Contrastive Learning Graph Domain Adaptation Multi View Contrastive Learning

June 7, 2023

Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning
Soyeong Jeong, Jinheon Baek, Sung Ju Hwang, Jong C. Park
Contrastive Learning Conversational Question Multi Turn Open Domain Conversational Question Answering Phrase Retrieval
On the Generalization of Multi-modal Contrastive Learning
Qi Zhang, Yifei Wang, Yisen Wang
Contrastive Learning Strong Generalization Multimodal Information Language Pair Self Supervised Contrastive Learning Modality Pair
ScoreCL: Augmentation-Adaptive Contrastive Learning via Score-Matching Function
Jin-Young Kim, Soonwoo Kwon, Hyojun Go, Yunsung Lee, Seungtaek Choi
Contrastive Learning Simple Function Score Matching Augmentation Method ImageNet LT
Rethinking Weak Supervision in Helping Contrastive Learning
Jingyi Cui, Weiran Huang, Yifei Wang, Yisen Wang
Contrastive Learning Semi Supervised Learning Weak Supervision

June 6, 2023

Systematic Analysis of Music Representations from BERT
Sangjun Han, Hyeongrae Ihm, Woohyung Lim
Contrastive Learning Jina Embeddings Ticket BERT Music Representation
Subgraph Networks Based Contrastive Learning
Jinhuan Wang, Jiafei Shao, Zeyu Wang, Shanqing Yu, Qi Xuan, Xiaoniu Yang
Contrastive Learning Graph Augmentation Multi Network Contrastive Learning
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs
Zhen Yang, Tinglin Huang, Ming Ding, Yuxiao Dong, Rex Ying, Yukuo Cen, Yangliao Geng, Jie Tang
Contrastive Learning Graph Drawing Human Language Mini Batch Contrastive Model Hard to Classify Instance
Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning
Chujie Zheng, Pei Ke, Zheng Zhang, Minlie Huang
Language Model Contrastive Learning Text Generation Contrastive Loss Sentiment Polarity Accidental Click Contrastive Data Sequence Likelihood
Unraveling Projection Heads in Contrastive Learning: Insights from Expansion and Shrinkage
Yu Gui, Cong Ma, Yiqiao Zhong
Contrastive Learning Contrastive Loss DCU Insight AQ Encoder Decoder Prompt Expansion Shrinkage Estimator Camera Projector Projection Head

June 5, 2023

Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training
Yibin Lei, Liang Ding, Yu Cao, Changtong Zan, Andrew Yates, Dacheng Tao
Contrastive Learning Contrastive Loss Dense Retrieval Dense Retriever Contrastive Pre Training Relevance Aware
Asymmetric Patch Sampling for Contrastive Learning
Chengchao Shen, Jianzhong Chen, Shu Wang, Hulin Kuang, Jin Liu, Jianxin Wang
Contrastive Learning Patch Sampling
ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction
Jia Guo, Shuai Lu, Lize Jia, Weihang Zhang, Huiqi Li
Contrastive Learning Anomaly Detection Unsupervised Anomaly Detection Medical Anomaly Detection Epistemic Neural Network

Contrastive Learning

Papers

DocumentCLIP: Linking Figures and Main Body Text in Reflowed Documents

Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression Values

Liquidity takers behavior representation through a contrastive learning approach

A brief review of contrastive learning applied to astrophysics

Factorized Contrastive Learning: Going Beyond Multi-view Redundancy

Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce

Contrastive Representation Disentanglement for Clustering

CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification

Phrase Retrieval for Open-Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning

On the Generalization of Multi-modal Contrastive Learning

ScoreCL: Augmentation-Adaptive Contrastive Learning via Score-Matching Function

Rethinking Weak Supervision in Helping Contrastive Learning

Systematic Analysis of Music Representations from BERT

Subgraph Networks Based Contrastive Learning

BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs

Click: Controllable Text Generation with Sequence Likelihood Contrastive Learning

Unraveling Projection Heads in Contrastive Learning: Insights from Expansion and Shrinkage

Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training

Asymmetric Patch Sampling for Contrastive Learning

ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction