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 5301–5350 of 6661 papers

TitleStatusHype
Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning—0
Image Difference Captioning with Pre-training and Contrastive LearningCode1
Point-Level Region Contrast for Object Detection Pre-TrainingCode1
Distillation with Contrast is All You Need for Self-Supervised Point Cloud Representation Learning—0
FMP: Toward Fair Graph Message Passing against Topology Bias—0
Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image SegmentationCode1
Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion—0
Self-supervised Contrastive Learning for Volcanic Unrest DetectionCode0
Self-supervised Contrastive Learning for Cross-domain Hyperspectral Image Representation—0
STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation—0
Learning Sound Localization Better From Semantically Similar Samples—0
Crafting Better Contrastive Views for Siamese Representation LearningCode2
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data AugmentationCode1
Self-supervised Speaker Recognition Training Using Human-Machine Dialogues—0
Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual RecognitionCode2
Hybrid Contrastive Quantization for Efficient Cross-View Video RetrievalCode1
Graph Self-supervised Learning with Accurate Discrepancy LearningCode1
Low-confidence Samples Matter for Domain AdaptationCode0
Intent Contrastive Learning for Sequential RecommendationCode1
Boundary-aware Information Maximization for Self-supervised Medical Image Segmentation—0
Quality Assessment of Low Light Restored Images: A Subjective Study and an Unsupervised Model—0
Supervised Contrastive Learning for Product MatchingCode1
The Met Dataset: Instance-level Recognition for Artworks—0
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series ForecastingCode2
Sim2Real Object-Centric Keypoint Detection and Description—0
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition—0
CIC: Contrastive Intrinsic Control for Unsupervised Skill DiscoveryCode1
HCSC: Hierarchical Contrastive Selective CodingCode1
Learning Robust Representation through Graph Adversarial Contrastive Learning—0
Learning to Hash Naturally Sorts—0
Contrastive Learning from Demonstrations—0
Generalizing similarity in noisy setups: the DIBS phenomenon—0
Self-Supervised Moving Vehicle Detection from Audio-Visual Cues—0
Investigating Why Contrastive Learning Benefits Robustness Against Label Noise—0
Understanding Deep Contrastive Learning via Coordinate-wise Optimization—0
SupWMA: Consistent and Efficient Tractography Parcellation of Superficial White Matter with Deep LearningCode1
Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification—0
Syfer: Neural Obfuscation for Private Data Release—0
PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence EmbeddingsCode1
Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked PositivesCode1
Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud ClassificationCode0
CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search—0
Pair-Level Supervised Contrastive Learning for Natural Language Inference—0
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks—0
Modeling Multi-level Context for Informational Bias Detection by Contrastive Learning and Sentential Graph Network—0
Cross-Domain Document Layout Analysis Using Document Style Guide—0
Learning Graph Augmentations to Learn Graph RepresentationsCode0
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
OntoProtein: Protein Pretraining With Gene Ontology EmbeddingCode1
PiCO+: Contrastive Label Disambiguation for Robust Partial Label LearningCode2
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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