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 5626–5650 of 6661 papers

TitleStatusHype
May the Dance be with You: Dance Generation Framework for Non-Humanoids—0
EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing—0
mcBERT: Momentum Contrastive Learning with BERT for Zero-Shot Slot Filling—0
MC-LCR: Multi-modal contrastive classification by locally correlated representations for effective face forgery detection—0
mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning—0
MCML: A Novel Memory-based Contrastive Meta-Learning Method for Few Shot Slot Tagging—0
MCoCo: Multi-level Consistency Collaborative Multi-view Clustering—0
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction—0
MDS-GNN: A Mutual Dual-Stream Graph Neural Network on Graphs with Incomplete Features and Structure—0
Measuring Pre-training Data Quality without Labels for Time Series Foundation Models—0
MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation—0
MedCoDi-M: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation—0
Med-gte-hybrid: A contextual embedding transformer model for extracting actionable information from clinical texts—0
Medical Report Generation based on Segment-Enhanced Contrastive Representation Learning—0
Medi-CAT: Contrastive Adversarial Training for Medical Image Classification—0
MedRAT: Unpaired Medical Report Generation via Auxiliary Tasks—0
MED-SE: Medical Entity Definition-based Sentence Embedding—0
Meeting Action Item Detection with Regularized Context Modeling—0
Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction—0
Memory Consistency Guided Divide-and-Conquer Learning for Generalized Category Discovery—0
Memory-efficient Continual Learning with Neural Collapse Contrastive—0
MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning—0
MERIT: Multilingual Semantic Retrieval with Interleaved Multi-Condition Query—0
MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels—0
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases—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