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 1751–1800 of 6661 papers

TitleStatusHype
Sequence-to-Sequence Contrastive Learning for Text RecognitionCode1
Camera-aware Proxies for Unsupervised Person Re-IdentificationCode1
Joint Generative and Contrastive Learning for Unsupervised Person Re-identificationCode1
ISD: Self-Supervised Learning by Iterative Similarity DistillationCode1
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert LinkingCode1
Contrastive Learning with Adversarial Perturbations for Conditional Text GenerationCode1
Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive LearningCode1
Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical ImagesCode1
Multi-Objective Interpolation Training for Robustness to Label NoiseCode1
Fine-grained Angular Contrastive Learning with Coarse LabelsCode1
Cross-Domain Sentiment Classification with In-Domain Contrastive LearningCode1
SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological ImagesCode1
Multi-level Knowledge Distillation via Knowledge Alignment and CorrelationCode1
Pre-Trained Image Processing TransformerCode1
Soft Contrastive Learning for Visual LocalizationCode1
Time Series Change Point Detection with Self-Supervised Contrastive Predictive CodingCode1
Self supervised contrastive learning for digital histopathologyCode1
USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation LearningCode1
CoMatch: Semi-supervised Learning with Contrastive Graph RegularizationCode1
Boosting Contrastive Self-Supervised Learning with False Negative CancellationCode1
Exploring Contrastive Learning in Human Activity Recognition for HealthcareCode1
Geography-Aware Self-Supervised LearningCode1
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation LearningCode1
Dense Contrastive Learning for Self-Supervised Visual Pre-TrainingCode1
Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive LearningCode1
Contrastive Registration for Unsupervised Medical Image SegmentationCode1
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative AdversariesCode1
Unsupervised Contrastive Learning of Sound Event RepresentationsCode1
CODER: Knowledge infused cross-lingual medical term embedding for term normalizationCode1
Learning and Evaluating Representations for Deep One-class ClassificationCode1
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuningCode1
Learning to Contrast the Counterfactual Samples for Robust Visual Question AnsweringCode1
Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information MaximizationCode1
Pretext-Contrastive Learning: Toward Good Practices in Self-supervised Video Representation LeaningCode1
Graph Contrastive Learning with Adaptive AugmentationCode1
Contrastive Learning for Sequential RecommendationCode1
Robust Pre-Training by Adversarial Contrastive LearningCode1
Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive LearningCode1
Multi-view Graph Contrastive Representation Learning for Drug-Drug Interaction PredictionCode1
Graph Contrastive Learning with AugmentationsCode1
Improving Transformation Invariance in Contrastive Representation LearningCode1
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function ApproximatorCode1
Self-supervised Co-training for Video Representation LearningCode1
i-Mix: A Domain-Agnostic Strategy for Contrastive Representation LearningCode1
Masked Contrastive Representation Learning for Reinforcement LearningCode1
Representation Learning via Invariant Causal MechanismsCode1
Viewmaker Networks: Learning Views for Unsupervised Representation LearningCode1
MixCo: Mix-up Contrastive Learning for Visual RepresentationCode1
Contrast and Classify: Training Robust VQA ModelsCode1
MS^2L: Multi-Task Self-Supervised Learning for Skeleton Based Action RecognitionCode1
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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