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 45264550 of 6661 papers

TitleStatusHype
Mutual Harmony: Sequential Recommendation with Dual Contrastive NetworkCode0
Few-Shot Classification with Contrastive Learning0
SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape CorrespondenceCode1
MetaMask: Revisiting Dimensional Confounder for Self-Supervised LearningCode1
Graph Contrastive Learning with Cross-view Reconstruction0
Spatial-then-Temporal Self-Supervised Learning for Video CorrespondenceCode1
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection0
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation0
FreeGaze: Resource-efficient Gaze Estimation via Frequency Domain Contrastive Learning0
Graph Contrastive Learning with Personalized Augmentation0
SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding0
Jointly Contrastive Representation Learning on Road Network and TrajectoryCode1
Joint Debiased Representation and Image Clustering Learning with Self-Supervision0
PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial AttackCode0
Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning0
Improving Self-Supervised Learning by Characterizing Idealized RepresentationsCode1
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity MatchingCode0
Don't Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention PoolingCode0
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural LanguageCode1
Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural FeaturesCode0
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive LearningCode0
Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection0
Ranking-Enhanced Unsupervised Sentence Representation LearningCode1
Information Maximization for Extreme Pose Face Recognition0
Semi-supervised Crowd Counting via Density AgencyCode1
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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