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 6
A Learnable Multi-views Contrastive Framework with Reconstruction Discrepancy for Medical Time-Series
Yifan Wang, Hongfeng Ai, Ruiqi Li, Maowei Jiang, Cheng Jiang, Chenzhong LiContrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to Global
Jinlu Wang, Yanfeng Sun, Jiapu Wang, Junbin Gao, Shaofan Wang, Jipeng Guo
Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Application
Gonzalo Iñaki Quintana, Laurence Vancamberg, Vincent Jugnon, Agnès Desolneux, Mathilde MougeotDFCon: Attention-Driven Supervised Contrastive Learning for Robust Deepfake Detection
MD Sadik Hossain Shanto, Mahir Labib Dihan, Souvik Ghosh, Riad Ahmed Anonto, Hafijul Hoque Chowdhury, Abir Muhtasim, Rakib Ahsan+4CSPCL: Category Semantic Prior Contrastive Learning for Deformable DETR-Based Prohibited Item Detectors
Mingyuan Li, Tong Jia, Hui Lu, Bowen Ma, Hao Wang, Dongyue Chen
2-Tier SimCSE: Elevating BERT for Robust Sentence Embeddings
Yumeng Wang, Ziran Zhou, Junjin WangMulti-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer
Jia Gao, Guiran Liu, Binrong Zhu, Shicheng Zhou, Hongye Zheng, Xiaoxuan LiaoRethinking the Sample Relations for Few-Shot Classification
Guowei Yin, Sheng Huang, Luwen Huangfu, Yi Zhang, Xiaohong Zhang
MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer Analysis
Daeun Jung, Jaehyeok Jang, Sooyoung Jang, Yu Rang ParkRobust Representation Consistency Model via Contrastive Denoising
Jiachen Lei, Julius Berner, Jiongxiao Wang, Zhongzhu Chen, Zhongjia Ba, Kui Ren, Jun Zhu, Anima Anandkumar