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 - Page 22
Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities
Lorenzo Baraldi, Federico Cocchi, Marcella Cornia, Lorenzo Baraldi, Alessandro Nicolosi, Rita CucchiaraImagiNet: A Multi-Content Benchmark for Synthetic Image Detection
Delyan Boychev, Radostin CholakovBoosting Graph Foundation Model from Structural Perspective
Yao Cheng, Yige Zhao, Jianxiang Yu, Xiang LiHashing based Contrastive Learning for Virtual Screening
Jin Han, Yun Hong, Wu-Jun LiContextuality Helps Representation Learning for Generalized Category Discovery
Tingzhang Luo, Mingxuan Du, Jiatao Shi, Xinxiang Chen, Bingchen Zhao, Shaoguang Huang
XLIP: Cross-modal Attention Masked Modelling for Medical Language-Image Pre-Training
Biao Wu, Yutong Xie, Zeyu Zhang, Minh Hieu Phan, Qi Chen, Ling Chen, Qi WuWeCromCL: Weakly Supervised Cross-Modality Contrastive Learning for Transcription-only Supervised Text Spotting
Jingjing Wu, Zhengyao Fang, Pengyuan Lyu, Chengquan Zhang, Fanglin Chen, Guangming Lu, Wenjie Pei
\mathbb{X}-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs
Vlad Sobal, Mark Ibrahim, Randall Balestriero, Vivien Cabannes, Diane Bouchacourt, Pietro Astolfi, Kyunghyun Cho, Yann LeCunBanyan: Improved Representation Learning with Explicit Structure
Mattia Opper, N. SiddharthYour Graph Recommender is Provably a Single-view Graph Contrastive Learning
Wenjie Yang, Shengzhong Zhang, Jiaxing Guo, Zengfeng Huang
Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift
Jiaqiang Zhang, Songcan ChenMasks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring
Shreyank N Gowda, David A. CliftonA Multi-view Mask Contrastive Learning Graph Convolutional Neural Network for Age Estimation
Yiping Zhang, Yuntao Shou, Tao Meng, Wei Ai, Keqin Li