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 4751–4800 of 6661 papers

TitleStatusHype
MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image SegmentationCode0
Open-Vocabulary 3D Detection via Image-level Class and Debiased Cross-modal Contrastive Learning—0
Features Based Adaptive Augmentation for Graph Contrastive LearningCode0
Block-SCL: Blocking Matters for Supervised Contrastive Learning in Product Matching—0
Multi-Modal Multi-Correlation Learning for Audio-Visual Speech Separation—0
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed RecognitionCode1
Game State Learning via Game Scene Augmentation—0
Positive-Negative Equal Contrastive Loss for Semantic Segmentation—0
Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasetsCode1
Multi-granularity Item-based Contrastive Recommendation—0
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly DetectionCode1
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot LearningCode1
Dynamic Contrastive Distillation for Image-Text Retrieval—0
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation Learning and Retrieval—0
GUIM -- General User and Item Embedding with Mixture of Representation in E-commerce—0
Contrastive Data and Learning for Natural Language Processing—0
Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection—0
RCL: Relation Contrastive Learning for Zero-Shot Relation ExtractionCode0
Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling—0
Label Refinement via Contrastive Learning for Distantly-Supervised Named Entity RecognitionCode0
ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations—0
Exploring Contrastive Learning for Multimodal Detection of Misogynistic MemesCode2
PALI at SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts—0
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis—0
Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent ThresholdCode0
Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based PredictionCode0
YNU-HPCC at SemEval-2022 Task 2: Representing Multilingual Idiomaticity based on Contrastive Learning—0
Learn from Relation Information: Towards Prototype Representation Rectification for Few-Shot Relation ExtractionCode0
TLDR at SemEval-2022 Task 1: Using Transformers to Learn Dictionaries and Representations—0
Studying the impact of magnitude pruning on contrastive learning methodsCode0
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning—0
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean LabelCode1
Reading and Writing: Discriminative and Generative Modeling for Self-Supervised Text RecognitionCode1
e-CLIP: Large-Scale Vision-Language Representation Learning in E-commerce—0
Self-Supervised Learning for Multimedia RecommendationCode1
Personalized Showcases: Generating Multi-Modal Explanations for Recommendations—0
TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT VolumesCode0
Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles—0
Interventional Contrastive Learning with Meta Semantic Regularizer—0
EBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering—0
Semi-supervised Contrastive Outlier removal for Pseudo Expectation Maximization (SCOPE)—0
FedIIC: Towards Robust Federated Learning for Class-Imbalanced Medical Image ClassificationCode1
Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation—0
Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark—0
Reducing Annotation Need in Self-Explanatory Models for Lung Nodule DiagnosisCode1
ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration—0
Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images AnalysisCode0
Vision Transformer for Contrastive ClusteringCode1
Wiener Graph Deconvolutional Network Improves Graph Self-Supervised LearningCode1
Show:102550
← PrevPage 96 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