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
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective
Di Jin, Jingyi Cao, Xiaobao Wang, Bingdao Feng, Dongxiao He, Longbiao Wang, Jianwu DangTianjin University●Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)●Chinese Academy of SciencesLocality-Sensitive Hashing for Efficient Hard Negative Sampling in Contrastive Learning
Fabian Deuser, Philipp Hausenblas, Hannah Schieber, Daniel Roth, Martin Werner, Norbert OswaldUniversity of the Bundeswehr Munich●Technical University of MunichBridging Electronic Health Records and Clinical Texts: Contrastive Learning for Enhanced Clinical Tasks
Sara Ketabi, Dhanesh RamachandramUniversity of Toronto●The Hospital for Sick Children●Vector Institute
Visual Perturbation and Adaptive Hard Negative Contrastive Learning for Compositional Reasoning in Vision-Language Models
Xin Huang, Ruibin Li, Tong Jia, Wei Zheng, Ya WangNanyang Normal University●Peking University●Collaborative Innovation Center of Intelligent Explosion-proof EquipmentKhan-GCL: Kolmogorov-Arnold Network Based Graph Contrastive Learning with Hard Negatives
Zihu Wang, Boxun Xu, Hejia Geng, Peng LiUniversity of California
Representation Learning for Semantic Alignment of Language, Audio, and Visual Modalities
Parthasaarathy Sudarsanam, Irene Martín-Morató, Tuomas VirtanenTampere UniversityAdapting Pretrained Language Models for Citation Classification via Self-Supervised Contrastive Learning
Tong Li, Jiachuan Wang, Yongqi Zhang, Shuangyin Li, Lei ChenHong Kong University of Science and Technology●Hong Kong University of Science and Technology (Guangzhou)●South China Normal UniversityTowards Generating Realistic Underwater Images
Abdul-Kazeem ShambaNorwegian University of Science and TechnologyFAID: Fine-grained AI-generated Text Detection using Multi-task Auxiliary and Multi-level Contrastive Learning
Minh Ngoc Ta, Dong Cao Van, Duc-Anh Hoang, Minh Le-Anh, Truong Nguyen, My Anh Tran Nguyen, Yuxia Wang, Preslav Nakov, Sang DinhBKAI Research Center●Hanoi University of Science and Technology●MBZUAI
The Computation of Generalized Embeddings for Underwater Acoustic Target Recognition using Contrastive Learning
Hilde I. Hummel, Arwin Gansekoele, Sandjai Bhulai, Rob van der MeiUnveil Multi-Picture Descriptions for Multilingual Mild Cognitive Impairment Detection via Contrastive Learning
Kristin Qi, Jiali Cheng, Youxiang Zhu, Hadi Amiri, Xiaohui LiangUniversity of Massachusetts
Contrastive Alignment with Semantic Gap-Aware Corrections in Text-Video Retrieval
Jian Xiao, Zijie Song, Jialong Hu, Hao Cheng, Zhenzhen Hu, Jia Li, Richang HongHefei University of TechnologyMulti-modal contrastive learning adapts to intrinsic dimensions of shared latent variables
Yu Gui, Cong Ma, Zongming MaUniversity of Chicago●Yale UniversityBridging Generative and Discriminative Learning: Few-Shot Relation Extraction via Two-Stage Knowledge-Guided Pre-training
Quanjiang Guo, Jinchuan Zhang, Sijie Wang, Ling Tian, Zhao Kang, Bin Yan, Weidong XiaoUniversity of Electronic Science and Technology of China●Nanyang Technological University●Information Engineering University●National...+1
Fine-Grained ECG-Text Contrastive Learning via Waveform Understanding Enhancement
Haitao Li, Che Liu, Zhengyao Ding, Ziyi Liu, Zhengxing HuangZhejiang University●Imperial College London●LtdDC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities
Haitao Li, Ziyu Li, Yiheng Mao, Zhengyao Ding, Zhengxing HuangZhejiang UniversityGenerative and Contrastive Graph Representation Learning
Jiali Chen, Avijit MukherjeeApple