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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4626–4650 of 6661 papers

TitleStatusHype
Siamese Prototypical Contrastive Learning—0
Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest RecommendationCode1
SelF-Eval: Self-supervised Fine-grained Dialogue EvaluationCode0
CommitBART: A Large Pre-trained Model for GitHub Commits—0
Multi-View Correlation Consistency for Semi-Supervised Semantic Segmentation—0
CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation—0
KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph CompletionCode1
DICE: Data-Efficient Clinical Event Extraction with Generative Models—0
Object Discovery via Contrastive Learning for Weakly Supervised Object DetectionCode1
C3-DINO: Joint Contrastive and Non-contrastive Self-Supervised Learning for Speaker Verification—0
ARIEL: Adversarial Graph Contrastive LearningCode0
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive LearningCode1
Multi-modal Siamese Network for Entity AlignmentCode1
Contrastive Learning for Joint Normal Estimation and Point Cloud FilteringCode0
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series ClassificationCode2
Enhancing Graph Contrastive Learning with Node Similarity—0
A Unified Two-Stage Group Semantics Propagation and Contrastive Learning Network for Co-Saliency Detection—0
Exploring High-quality Target Domain Information for Unsupervised Domain Adaptive Semantic SegmentationCode1
Contrastive Learning for OOD in Object detectionCode0
Contrastive Learning for Object DetectionCode0
Motion Sensitive Contrastive Learning for Self-supervised Video Representation—0
RenyiCL: Contrastive Representation Learning with Skew Renyi DivergenceCode1
Path-aware Siamese Graph Neural Network for Link PredictionCode0
Self-supervised Multi-modal Training from Uncurated Image and Reports Enables Zero-shot Oversight Artificial Intelligence in RadiologyCode0
Multi-View Pre-Trained Model for Code Vulnerability Identification—0
Show:102550
← PrevPage 186 of 267Next →
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