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
Anomalies, Representations, and Self-Supervision
Barry M. Dillon, Luigi Favaro, Friedrich Feiden, Tanmoy Modak, Tilman Plehn
Generative-Contrastive Learning for Self-Supervised Latent Representations of 3D Shapes from Multi-Modal Euclidean Input
Chengzhi Wu, Julius Pfrommer, Mingyuan Zhou, Jürgen Beyerer
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis
Hong-Yu Zhou, Chixiang Lu, Liansheng Wang, Yizhou Yu
Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification
Meng Wang, Feng Gao, Junyu Dong, Heng-Chao Li, Qian Du
Simplifying Open-Set Video Domain Adaptation with Contrastive Learning
Giacomo Zara, Victor Guilherme Turrisi da Costa, Subhankar Roy, Paolo Rota, Elisa Ricci
Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning
Jidong Ge, Yuxiang Liu, Jie Gui, Lanting Fang, Ming Lin, James Tin-Yau Kwok, LiGuo Huang, Bin Luo
Mitigating Human and Computer Opinion Fraud via Contrastive Learning
Yuliya Tukmacheva, Ivan Oseledets, Evgeny Frolov
Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training
Filip Radenovic, Abhimanyu Dubey, Abhishek Kadian, Todor Mihaylov, Simon Vandenhende, Yash Patel, Yi Wen, Vignesh Ramanathan, Dhruv Mahajan
Learning by Sorting: Self-supervised Learning with Group Ordering Constraints
Nina Shvetsova, Felix Petersen, Anna Kukleva, Bernt Schiele, Hilde Kuehne
SIRL: Similarity-based Implicit Representation Learning
Andreea Bobu, Yi Liu, Rohin Shah, Daniel S. Brown, Anca D. Dragan
Learning Invariance from Generated Variance for Unsupervised Person Re-identification
Hao Chen, Yaohui Wang, Benoit Lagadec, Antitza Dantcheva, Francois Bremond
A contrastive learning approach for individual re-identification in a wild fish population
Ørjan Langøy Olsen, Tonje Knutsen Sørdalen, Morten Goodwin, Ketil Malde, Kristian Muri Knausgård, Kim Tallaksen Halvorsen