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 2551–2575 of 6661 papers

TitleStatusHype
GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive LearningCode0
What Has Been Overlooked in Contrastive Source-Free Domain Adaptation: Leveraging Source-Informed Latent Augmentation within Neighborhood ContextCode0
Multi-view Granular-ball Contrastive Clustering—0
Multi-Domain Features Guided Supervised Contrastive Learning for Radar Target Detection—0
Cluster-guided Contrastive Class-imbalanced Graph Classification—0
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning—0
ClarityEthic: Explainable Moral Judgment Utilizing Contrastive Ethical Insights from Large Language Models—0
Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRecCode0
Multi-head attention debiasing and contrastive learning for mitigating Dataset Artifacts in Natural Language Inference—0
UniLoc: Towards Universal Place Recognition Using Any Single Modality—0
CLDA-YOLO: Visual Contrastive Learning Based Domain Adaptive YOLO Detector—0
Personalized LLM for Generating Customized Responses to the Same Query from Different UsersCode0
Temporal Contrastive Learning for Video Temporal Reasoning in Large Vision-Language Models—0
Generalization Analysis for Deep Contrastive Representation Learning—0
SE-GCL: An Event-Based Simple and Effective Graph Contrastive Learning for Text Representation—0
Exploring Temporal Event Cues for Dense Video Captioning in Cyclic Co-learning—0
Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced RegressionCode0
Leveraging Retrieval-Augmented Tags for Large Vision-Language Understanding in Complex Scenes—0
Segment-Level Diffusion: A Framework for Controllable Long-Form Generation with Diffusion Language Models—0
RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models—0
Edge Contrastive Learning: An Augmentation-Free Graph Contrastive Learning ModelCode0
Multi-Graph Co-Training for Capturing User Intent in Session-based RecommendationCode0
CATALOG: A Camera Trap Language-guided Contrastive Learning ModelCode0
Label-template based Few-Shot Text Classification with Contrastive Learning—0
A dual contrastive framework—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