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 4576–4600 of 6661 papers

TitleStatusHype
Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax—0
Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction—0
PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud RegistrationCode1
Focus-Driven Contrastive Learniang for Medical Question SummarizationCode0
Let Me Check the Examples: Enhancing Demonstration Learning via Explicit Imitation—0
Supervised Contrastive Learning with Hard Negative SamplesCode1
Incorporating Task-specific Concept Knowledge into Script LearningCode0
EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud ComputingCode1
SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual CategorizationCode1
Optimizing Bi-Encoder for Named Entity Recognition via Contrastive LearningCode1
Compound Figure Separation of Biomedical Images: Mining Large Datasets for Self-supervised LearningCode0
Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation—0
Learning Informative Health Indicators Through Unsupervised Contrastive Learning—0
ContrastVAE: Contrastive Variational AutoEncoder for Sequential RecommendationCode1
Multi-level Contrastive Learning Framework for Sequential Recommendation—0
CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic SegmentationCode1
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning—0
Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning—0
Decoding speech perception from non-invasive brain recordingsCode2
Supervised Contrastive Learning for Affect ModellingCode0
Multimedia Generative Script Learning for Task PlanningCode0
Skeleton Prototype Contrastive Learning with Multi-Level Graph Relation Modeling for Unsupervised Person Re-IdentificationCode0
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis—0
Learning crop type mapping from regional label proportions in large-scale SAR and optical imagery—0
Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models—0
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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