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 6051–6100 of 6661 papers

TitleStatusHype
Sentence Embeddings using Supervised Contrastive LearningCode0
Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive LossCode1
Contrastive Representation Learning for Hand Shape Estimation—0
Learning Markov State Abstractions for Deep Reinforcement LearningCode1
Shifting Transformation Learning for Out-of-Distribution Detection—0
Socially-Aware Self-Supervised Tri-Training for RecommendationCode2
Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast—0
Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast—0
Mean-Shifted Contrastive Loss for Anomaly DetectionCode1
Incremental False Negative Detection for Contrastive Learning—0
Self-Damaging Contrastive LearningCode1
Understand and Improve Contrastive Learning Methods for Visual Representation: A Review—0
MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular GraphCode1
Integrating Auxiliary Information in Self-supervised Learning—0
Category Contrast for Unsupervised Domain Adaptation in Visual TasksCode1
Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning—0
Graph Barlow Twins: A self-supervised representation learning framework for graphsCode1
Aligning Pretraining for Detection via Object-Level Contrastive LearningCode1
Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene—0
Self-Guided Contrastive Learning for BERT Sentence RepresentationsCode1
TVDIM: Enhancing Image Self-Supervised Pretraining via Noisy Text Data—0
SimCLS: A Simple Framework for Contrastive Learning of Abstractive SummarizationCode1
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation—0
You Never Cluster Alone—0
Adversarial Self-Supervised Learning for Out-of-Domain DetectionCode0
NewsEmbed: Modeling News through Pre-trained Document Representations—0
Toward Understanding the Feature Learning Process of Self-supervised Contrastive Learning—0
CLEVE: Contrastive Pre-training for Event ExtractionCode1
Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive LearningCode1
Lightweight Cross-Lingual Sentence Representation LearningCode0
CoSQA: 20,000+ Web Queries for Code Search and Question AnsweringCode1
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
ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation TransferCode1
Unsupervised Visual Representation Learning by Online Constrained K-MeansCode1
Improving Contrastive Learning on Imbalanced Data via Open-World SamplingCode1
Contrastive Learning for Many-to-many Multilingual Neural Machine TranslationCode1
More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching—0
Balancing Robustness and Sensitivity using Feature Contrastive Learning—0
Heterogeneous Contrastive LearningCode0
Self-supervised Heterogeneous Graph Neural Network with Co-contrastive LearningCode1
Self-supervised Remote Sensing Images Change Detection at Pixel-level—0
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Masked Contrastive Learning for Anomaly DetectionCode1
Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation—0
Large-Scale Unsupervised Person Re-Identification with Contrastive Learning—0
Divide and Contrast: Self-supervised Learning from Uncurated Data—0
Voxel-level Siamese Representation Learning for Abdominal Multi-Organ Segmentation—0
TCL: Transformer-based Dynamic Graph Modelling via Contrastive LearningCode1
Show:102550
← PrevPage 122 of 134Next →
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