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 2051–2075 of 6661 papers

TitleStatusHype
Disentangle Perceptual Learning through Online Contrastive Learning—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
Attention-wise masked graph contrastive learning for predicting molecular property—0
Disentangled Graph Contrastive Learning for Review-based Recommendation—0
cMIM: A Contrastive Mutual Information Framework for Unified Generative and Discriminative Representation Learning—0
Few-shot Font Generation by Learning Style Difference and Similarity—0
Disentangled Contrastive Learning on Graphs—0
Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images—0
Disentangled Contrastive Image Translation for Nighttime Surveillance—0
CMAL: A Novel Cross-Modal Associative Learning Framework for Vision-Language Pre-Training—0
Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning—0
Few-shot Implicit Function Generation via Equivariance—0
Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking—0
Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce—0
Discriminative Speaker Representation via Contrastive Learning with Class-Aware Attention in Angular Space—0
Cluster Specific Representation Learning—0
Discrete Contrastive Learning for Diffusion Policies in Autonomous Driving—0
Few-Example Clustering via Contrastive Learning—0
Few-Shot Classification with Contrastive Learning—0
Discrepant and Multi-Instance Proxies for Unsupervised Person Re-Identification—0
Clustering-friendly Representation Learning for Enhancing Salient Features—0
Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration—0
Discovering COVID-19 Coughing and Breathing Patterns from Unlabeled Data Using Contrastive Learning with Varying Pre-Training Domains—0
Attention Mechanism for Contrastive Learning in GAN-based Image-to-Image Translation—0
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation—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