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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5801–5825 of 6661 papers

TitleStatusHype
Multi-Modal Representation Learning with Text-Driven Soft Masks—0
Multimodal self-supervised learning for lesion localization—0
Multimodal Short Video Rumor Detection System Based on Contrastive Learning—0
Multi-modal Spatial Clustering for Spatial Transcriptomics Utilizing High-resolution Histology Images—0
Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection—0
Multi-Modal Video Topic Segmentation with Dual-Contrastive Domain Adaptation—0
Multi-modal Vision Pre-training for Medical Image Analysis—0
Multi-network Contrastive Learning Based on Global and Local Representations—0
Multi-object event graph representation learning for Video Question Answering—0
Multi-organ Self-supervised Contrastive Learning for Breast Lesion Segmentation—0
Multi-perspective Contrastive Logit Distillation—0
Multiple Consistency-guided Test-Time Adaptation for Contrastive Audio-Language Models with Unlabeled Audio—0
Multi Positive Contrastive Learning with Pose-Consistent Generated Images—0
MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast—0
Multiscale Adaptive Conflict-Balancing Model For Multimedia Deepfake Detection—0
Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series—0
Multi-scale Contrastive Adaptor Learning for Segmenting Anything in Underperformed Scenes—0
Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction—0
Multi-Scale Contrastive Learning for Video Temporal Grounding—0
Multi-Scale Cross Contrastive Learning for Semi-Supervised Medical Image Segmentation—0
Multi-scale frequency separation network for image deblurring—0
Multiscale Matching Driven by Cross-Modal Similarity Consistency for Audio-Text Retrieval—0
Multi-scale multi-modal micro-expression recognition algorithm based on transformer—0
Multiscale Progressive Text Prompt Network for Medical Image Segmentation—0
Multi-Scale Self-Contrastive Learning with Hard Negative Mining for Weakly-Supervised Query-based Video Grounding—0
Show:102550
← PrevPage 233 of 267Next →
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