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 5376–5400 of 6661 papers

TitleStatusHype
Learning Informative Health Indicators Through Unsupervised Contrastive Learning—0
Multi-level Contrastive Learning Framework for Sequential Recommendation—0
Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning—0
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning—0
Supervised Contrastive Learning for Affect ModellingCode0
Skeleton Prototype Contrastive Learning with Multi-Level Graph Relation Modeling for Unsupervised Person Re-IdentificationCode0
Multimedia Generative Script Learning for Task PlanningCode0
Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation—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
Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis—0
Bidirectional Contrastive Split Learning for Visual Question Answering—0
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning—0
IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue ClassificationCode0
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations—0
Spiral Contrastive Learning: An Efficient 3D Representation Learning Method for Unannotated CT Lesions—0
Bitext Mining for Low-Resource Languages via Contrastive LearningCode0
Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image SegmentationCode0
Anatomy-Aware Contrastive Representation Learning for Fetal UltrasoundCode0
TaCo: Textual Attribute Recognition via Contrastive Learning—0
Patient-level Microsatellite Stability Assessment from Whole Slide Images By Combining Momentum Contrast Learning and Group Patch EmbeddingsCode0
Masked Video Modeling with Correlation-aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound—0
CODER: Coupled Diversity-Sensitive Momentum Contrastive Learning for Image-Text Retrieval—0
CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations—0
GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization—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