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 5601–5650 of 6661 papers

TitleStatusHype
MarginNCE: Robust Sound Localization with a Negative Margin—0
MaRI: Material Retrieval Integration across Domains—0
MarkMatch: Same-Hand Stuffing Detection—0
Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog—0
Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation—0
Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors—0
MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning—0
Masked Audio Text Encoders are Effective Multi-Modal Rescorers—0
Masked Autoencoders As The Unified Learners For Pre-Trained Sentence Representation—0
Masked Contrastive Reconstruction for Cross-modal Medical Image-Report Retrieval—0
Masked Contrastive Representation Learning—0
Masked Image Contrastive Learning for Efficient Visual Conceptual Pre-training—0
Masked Image Modeling Advances 3D Medical Image Analysis—0
Masked LoGoNet: Fast and Accurate 3D Image Analysis for Medical Domain—0
Masked Momentum Contrastive Learning for Zero-shot Semantic Understanding—0
Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding—0
Masked Reconstruction Contrastive Learning with Information Bottleneck Principle—0
Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learning—0
Masked Video Modeling with Correlation-aware Contrastive Learning for Breast Cancer Diagnosis in Ultrasound—0
Mask-guided BERT for Few Shot Text Classification—0
Masks and Manuscripts: Advancing Medical Pre-training with End-to-End Masking and Narrative Structuring—0
Massively Scaling Heteroscedastic Classifiers—0
MatExpert: Decomposing Materials Discovery by Mimicking Human Experts—0
X-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs—0
Mathematical Justification of Hard Negative Mining via Isometric Approximation Theorem—0
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
Show:102550
← PrevPage 113 of 134Next →
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