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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6126–6150 of 6661 papers

TitleStatusHype
ReactioNet: Learning High-Order Facial Behavior from Universal Stimulus-Reaction by Dyadic Relation Reasoning—0
ReadE: Learning Relation-Dependent Entity Representation for Knowledge Graph Completion—0
RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning—0
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning—0
Real-Time Idling Vehicles Detection using Combined Audio-Visual Deep Learning—0
Real-time Seismic Intensity Prediction using Self-supervised Contrastive GNN for Earthquake Early Warning—0
Real-world Instance-specific Image Goal Navigation: Bridging Domain Gaps via Contrastive Learning—0
Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition—0
Recent Advancement of Emotion Cognition in Large Language Models—0
RECLIP: Resource-efficient CLIP by Training with Small Images—0
Recommendation System in Advertising and Streaming Media: Unsupervised Data Enhancement Sequence Suggestions—0
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks—0
ReConTab: Regularized Contrastive Representation Learning for Tabular Data—0
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification—0
Reducing Distraction in Long-Context Language Models by Focused Learning—0
Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach—0
Refine Knowledge of Large Language Models via Adaptive Contrastive Learning—0
RefineVIS: Video Instance Segmentation with Temporal Attention Refinement—0
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks—0
Refining Self-Supervised Learning in Imaging: Beyond Linear Metric—0
Region-aware Knowledge Distillation for Efficient Image-to-Image Translation—0
Region-based Contrastive Pretraining for Medical Image Retrieval with Anatomic Query—0
Registering Neural Radiance Fields as 3D Density Images—0
Regressing Transformers for Data-efficient Visual Place Recognition—0
Show:102550
← PrevPage 246 of 267Next →
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