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 1051–1100 of 6661 papers

TitleStatusHype
ALSO: Automotive Lidar Self-supervision by Occupancy estimationCode1
Towards Cross-Table Masked Pretraining for Web Data MiningCode1
CSGCL: Community-Strength-Enhanced Graph Contrastive LearningCode1
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID DataCode1
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted InstancesCode1
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
Data Augmenting Contrastive Learning of Speech Representations in the Time DomainCode1
ProGCL: Rethinking Hard Negative Mining in Graph Contrastive LearningCode1
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-ResolutionCode1
Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental ConditionsCode1
Cross-modal Contrastive Learning for Speech TranslationCode1
Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud RegistrationCode1
Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image SegmentationCode1
ContraCLM: Contrastive Learning For Causal Language ModelCode1
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive AlignmentCode1
Cross-Modal Retrieval with Partially Mismatched PairsCode1
CRIS: CLIP-Driven Referring Image SegmentationCode1
Enhancing Text-based Knowledge Graph Completion with Zero-Shot Large Language Models: A Focus on Semantic EnhancementCode1
CP2: Copy-Paste Contrastive Pretraining for Semantic SegmentationCode1
CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language AlignmentCode1
CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked AutoencodersCode1
ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text EmbeddingsCode1
CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-ThoughtCode1
Contrastive Domain Adaptation for Time-Series via Temporal MixupCode1
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and BeyondCode1
ContraBAR: Contrastive Bayes-Adaptive Deep RLCode1
Counterfactual contrastive learning: robust representations via causal image synthesisCode1
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule DiagnosisCode1
Cross-Architecture Self-supervised Video Representation LearningCode1
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information MaximizationCode1
Cross-Domain Sentiment Classification with In-Domain Contrastive LearningCode1
Cross-modal Causal Relation Alignment for Video Question GroundingCode1
Cross-modal Contrastive Learning for Multimodal Fake News DetectionCode1
Cross-Modal Contrastive Learning for Text-to-Image GenerationCode1
ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation TransferCode1
Contrast and Generation Make BART a Good Dialogue Emotion RecognizerCode1
ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design ModelsCode1
Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IANCode1
Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-trainingCode1
Consistent Explanations by Contrastive LearningCode1
Consistent Representation Learning for Continual Relation ExtractionCode1
Contrast and Classify: Training Robust VQA ModelsCode1
CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal ConversionCode1
Cross-Patch Dense Contrastive Learning for Semi-Supervised Segmentation of Cellular Nuclei in Histopathologic ImagesCode1
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical ImagesCode1
Learning the Unlearned: Mitigating Feature Suppression in Contrastive LearningCode1
CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image SegmentationCode1
Correspondence Matters for Video Referring Expression ComprehensionCode1
Automatic Biomedical Term Clustering by Learning Fine-grained Term RepresentationsCode1
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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