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 15011525 of 6661 papers

TitleStatusHype
Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based FeaturesCode1
Contrastive Viewpoint-aware Shape Learning for Long-term Person Re-IdentificationCode1
Correspondence Matters for Video Referring Expression ComprehensionCode1
3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised LearningCode1
MS^2L: Multi-Task Self-Supervised Learning for Skeleton Based Action RecognitionCode1
Mugs: A Multi-Granular Self-Supervised Learning FrameworkCode1
Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical ImagesCode1
ContrastNet: A Contrastive Learning Framework for Few-Shot Text ClassificationCode1
Contrast-Phys+: Unsupervised and Weakly-supervised Video-based Remote Physiological Measurement via Spatiotemporal ContrastCode1
Capsule Network based Contrastive Learning of Unsupervised Visual RepresentationsCode1
CoSQA: 20,000+ Web Queries for Code Search and Question AnsweringCode1
Contrast then Memorize: Semantic Neighbor Retrieval-Enhanced Inductive Multimodal Knowledge Graph CompletionCode1
Multi-Label Guided Soft Contrastive Learning for Efficient Earth Observation PretrainingCode1
ContrastVAE: Contrastive Variational AutoEncoder for Sequential RecommendationCode1
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly DetectionCode1
Multi-level Knowledge Distillation via Knowledge Alignment and CorrelationCode1
Multimodal contrastive learning for spatial gene expression prediction using histology imagesCode1
Multimodal Contrastive Learning of Urban Space Representations from POI DataCode1
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity RecognitionCode1
Multi-Modality Co-Learning for Efficient Skeleton-based Action RecognitionCode1
Does Learning from Decentralized Non-IID Unlabeled Data Benefit from Self Supervision?Code1
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive LearningCode1
CaseGNN++: Graph Contrastive Learning for Legal Case Retrieval with Graph AugmentationCode1
Deep Graph Contrastive Representation LearningCode1
Dual Contrastive Learning: Text Classification via Label-Aware Data AugmentationCode1
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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