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 3901–3925 of 6661 papers

TitleStatusHype
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset SelectionCode0
Multi-view Feature Extraction based on Dual Contrastive Head—0
Continuous Learning for Android Malware DetectionCode1
Diagnosing and Rectifying Vision Models using LanguageCode1
SimCGNN: Simple Contrastive Graph Neural Network for Session-based Recommendation—0
Disentangled Causal Embedding With Contrastive Learning For Recommender SystemCode1
Cluster-Level Contrastive Learning for Emotion Recognition in ConversationsCode1
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining ApproachCode1
APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning—0
Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking—0
Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning—0
Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous InputsCode1
Spectral Augmentations for Graph Contrastive Learning—0
Cluster-aware Contrastive Learning for Unsupervised Out-of-distribution Detection—0
Adversarial Learning Data Augmentation for Graph Contrastive Learning in RecommendationCode0
Rethinking Robust Contrastive Learning from the Adversarial PerspectiveCode0
Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization—0
CIPER: Combining Invariant and Equivariant Representations Using Contrastive and Predictive Learning—0
Pyramid Self-attention Polymerization Learning for Semi-supervised Skeleton-based Action RecognitionCode0
Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semi-Supervised Skeleton-based Action Recognition—0
Contrastive Collaborative Filtering for Cold-Start Item RecommendationCode1
Transform, Contrast and Tell: Coherent Entity-Aware Multi-Image CaptioningCode0
MOMA:Distill from Self-Supervised Teachers—0
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction PerspectiveCode1
Style Feature Extraction Using Contrastive Conditioned Variational Autoencoders with Mutual Information Constraints—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