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 6326–6350 of 6661 papers

TitleStatusHype
Cross-domain Contrastive Learning for Unsupervised Domain AdaptationCode0
Fairness-Aware Node Representation Learning—0
Sentence Embeddings using Supervised Contrastive LearningCode0
Contrastive Representation Learning for Hand Shape Estimation—0
Shifting Transformation Learning for Out-of-Distribution Detection—0
Incremental False Negative Detection for Contrastive Learning—0
Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast—0
Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast—0
Understand and Improve Contrastive Learning Methods for Visual Representation: A Review—0
Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning—0
Integrating Auxiliary Information in Self-supervised Learning—0
Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene—0
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation—0
TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data—0
You Never Cluster Alone—0
NewsEmbed: Modeling News through Pre-trained Document Representations—0
Adversarial Self-Supervised Learning for Out-of-Domain DetectionCode0
Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning—0
Lightweight Cross-Lingual Sentence Representation LearningCode0
Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation—0
Provable Representation Learning for Imitation with Contrastive Fourier Features—0
GeomCA: Geometric Evaluation of Data RepresentationsCode0
More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching—0
Heterogeneous Contrastive LearningCode0
Balancing Robustness and Sensitivity using Feature Contrastive Learning—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