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

Showing 13761400 of 6661 papers

TitleStatusHype
Disentangling Long and Short-Term Interests for RecommendationCode1
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence LearningCode1
Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm PerformanceCode1
Indiscriminate Poisoning Attacks on Unsupervised Contrastive LearningCode1
Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment ContrastCode1
Towards better understanding and better generalization of few-shot classification in histology images with contrastive learningCode1
Contrastive Meta Learning with Behavior Multiplicity for RecommendationCode1
Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive LearningCode1
Learning Weakly-Supervised Contrastive RepresentationsCode1
Neighborhood Contrastive Learning for Scientific Document Representations with Citation EmbeddingsCode1
Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive LearningCode1
What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?Code1
Metadata-Induced Contrastive Learning for Zero-Shot Multi-Label Text ClassificationCode1
Conditional Contrastive Learning with KernelCode1
Energy-Based Contrastive Learning of Visual RepresentationsCode1
Point-Level Region Contrast for Object Detection Pre-TrainingCode1
Image Difference Captioning with Pre-training and Contrastive LearningCode1
Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image SegmentationCode1
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data AugmentationCode1
Graph Self-supervised Learning with Accurate Discrepancy LearningCode1
Hybrid Contrastive Quantization for Efficient Cross-View Video RetrievalCode1
Intent Contrastive Learning for Sequential RecommendationCode1
Supervised Contrastive Learning for Product MatchingCode1
CIC: Contrastive Intrinsic Control for Unsupervised Skill DiscoveryCode1
HCSC: Hierarchical Contrastive Selective CodingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified