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
Contrastive Learning for Climate Model Bias Correction and Super-Resolution
Tristan Ballard, Gopal Erinjippurath
Self-supervised learning of audio representations using angular contrastive loss
Shanshan Wang, Soumya Tripathy, Annamaria Mesaros
Mitigating Forgetting in Online Continual Learning via Contrasting Semantically Distinct Augmentations
Sheng-Feng Yu, Wei-Chen Chiu
Clinical Contrastive Learning for Biomarker Detection
Kiran Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib
Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language
Denys Amore Bondarenko, Roger Ferrod, Luigi Di Caro
miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings
Tassilo Klein, Moin Nabi
ConsPrompt: Exploiting Contrastive Samples for Fewshot Prompt Learning
Jinta Weng, Yifan Deng, d Donghao Li, Hao You, Yue Hu, Heyan Huang
Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data
Yiming Wang, Dongxia Chang, Zhiqiang Fu, Jie Wen, Yao Zhao
Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning
Qian Li, Shafiq Joty, Daling Wang, Shi Feng, Yifei Zhang
Graph Contrastive Learning with Implicit Augmentations
Huidong Liang, Xingjian Du, Bilei Zhu, Zejun Ma, Ke Chen, Junbin Gao
CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI
Li-Hsiang Shen, Chia-Che Hsieh, An-Hung Hsiao, Kai-Ten Feng
Contrastive Classification and Representation Learning with Probabilistic Interpretation
Rahaf Aljundi, Yash Patel, Milan Sulc, Daniel Olmeda, Nikolay Chumerin
Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions
Thong Nguyen, Xiaobao Wu, Anh-Tuan Luu, Cong-Duy Nguyen, Zhen Hai, Lidong Bing
Black-Box Attack against GAN-Generated Image Detector with Contrastive Perturbation
Zijie Lou, Gang Cao, Man Lin
Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding
Jiali Zeng, Yongjing Yin, Yufan Jiang, Shuangzhi Wu, Yunbo Cao
Contrastive Learning enhanced Author-Style Headline Generation
Hui Liu, Weidong Guo, Yige Chen, Xiangyang Li