SOTAVerified

Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

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Showing 651–675 of 6661 papers

TitleStatusHype
A Simple Graph Contrastive Learning Framework for Short Text ClassificationCode1
Efficient Few-Shot Medical Image Analysis via Hierarchical Contrastive Vision-Language Learning—0
Towards Robust and Realistic Human Pose Estimation via WiFi SignalsCode1
Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive LearningCode0
Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention for Image Restoration Models Compression—0
Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data—0
Pseudolabel guided pixels contrast for domain adaptive semantic segmentationCode0
Molecular Graph Contrastive Learning with Line GraphCode0
Homophily-aware Heterogeneous Graph Contrastive Learning—0
SHYI: Action Support for Contrastive Learning in High-Fidelity Text-to-Image Generation—0
TCMM: Token Constraint and Multi-Scale Memory Bank of Contrastive Learning for Unsupervised Person Re-identificationCode0
Benchmarking Robustness of Contrastive Learning Models for Medical Image-Report Retrieval—0
Vision Foundation Models for Computed TomographyCode2
Advancing Brainwave-Based Biometrics: A Large-Scale, Multi-Session EvaluationCode0
FLAVARS: A Multimodal Foundational Language and Vision Alignment Model for Remote Sensing—0
Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders—0
Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation—0
Code and Pixels: Multi-Modal Contrastive Pre-training for Enhanced Tabular Data Analysis—0
ACCon: Angle-Compensated Contrastive Regularizer for Deep Regression—0
Robust Single Object Tracking in LiDAR Point Clouds under Adverse Weather Conditions—0
Graph Contrastive Learning on Multi-label Classification for Recommendations—0
Evaluating unsupervised contrastive learning framework for MRI sequences classification—0
Language-Inspired Relation Transfer for Few-shot Class-Incremental Learning—0
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning—0
Learning Compact and Robust Representations for Anomaly Detection—0
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All datasetsimagenet-1k10,000 People - Human Pose Recognition DataCIFAR-10STL-10

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6—Unverified
2ResNet50ImageNet Top-1 Accuracy73—Unverified
3ResNet50ImageNet Top-1 Accuracy71.1—Unverified
4ResNet50ImageNet Top-1 Accuracy69.3—Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6—Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8—Unverified
7ResNet50ImageNet Top-1 Accuracy63.6—Unverified
8ResNet50ImageNet Top-1 Accuracy61.5—Unverified
9ResNet50ImageNet Top-1 Accuracy61.5—Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3—Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77—Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55—Unverified