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

Showing 10761100 of 6661 papers

TitleStatusHype
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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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified