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 1151–1200 of 6661 papers

TitleStatusHype
Contrastive Semi-supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical StructuresCode1
Reproducibility Report: Contrastive Learning of Socially-aware Motion RepresentationsCode1
MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D SegmentationCode1
Mere Contrastive Learning for Cross-Domain Sentiment AnalysisCode1
Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest RecommendationCode1
Object Discovery via Contrastive Learning for Weakly Supervised Object DetectionCode1
KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph CompletionCode1
Hierarchical Attention Network for Few-Shot Object Detection via Meta-Contrastive LearningCode1
Multi-modal Siamese Network for Entity AlignmentCode1
RenyiCL: Contrastive Representation Learning with Skew Renyi DivergenceCode1
Exploring High-quality Target Domain Information for Unsupervised Domain Adaptive Semantic SegmentationCode1
Contrastive Learning with Bidirectional Transformers for Sequential RecommendationCode1
OpenCon: Open-world Contrastive LearningCode1
XCon: Learning with Experts for Fine-grained Category DiscoveryCode1
COCOA: Cross Modality Contrastive Learning for Sensor DataCode1
Multimodal SuperCon: Classifier for Drivers of Deforestation in IndonesiaCode1
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
Contrastive Masked Autoencoders are Stronger Vision LearnersCode1
Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative MiningCode1
Semi-supervised 3D Object Detection with Proficient TeachersCode1
Generative Subgraph Contrast for Self-Supervised Graph Representation LearningCode1
Online Knowledge Distillation via Mutual Contrastive Learning for Visual RecognitionCode1
Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosisCode1
Adaptive Soft Contrastive LearningCode1
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial RobustnessCode1
Correspondence Matters for Video Referring Expression ComprehensionCode1
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action RecognitionCode1
Hierarchically Self-Supervised Transformer for Human Skeleton Representation LearningCode1
Feature Representation Learning for Unsupervised Cross-domain Image RetrievalCode1
FedX: Unsupervised Federated Learning with Cross Knowledge DistillationCode1
Simplified Transfer Learning for Chest Radiography Models Using Less DataCode1
Balanced Contrastive Learning for Long-Tailed Visual RecognitionCode1
FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANsCode1
Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI AcquisitionsCode1
Adversarial Contrastive Learning via Asymmetric InfoNCECode1
Fast-MoCo: Boost Momentum-based Contrastive Learning with Combinatorial PatchesCode1
FashionViL: Fashion-Focused Vision-and-Language Representation LearningCode1
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text RetrievalCode1
Deep Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional NetworksCode1
Benchmarking Omni-Vision Representation through the Lens of Visual RealmsCode1
ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology ImagesCode1
Contrastive Deep SupervisionCode1
Self-supervised Group Meiosis Contrastive Learning for EEG-Based Emotion RecognitionCode1
Towards Proper Contrastive Self-supervised Learning Strategies For Music Audio RepresentationCode1
Sudowoodo: Contrastive Self-supervised Learning for Multi-purpose Data Integration and PreparationCode1
DLME: Deep Local-flatness Manifold EmbeddingCode1
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action RecognitionCode1
Network Binarization via Contrastive LearningCode1
Integrating multi-label contrastive learning with dual adversarial graph neural networks for cross-modal retrievalCode1
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
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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