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 1101–1150 of 6661 papers

TitleStatusHype
Constructing Tree-based Index for Efficient and Effective Dense RetrievalCode1
Adaptive Graph Contrastive Learning for RecommendationCode1
Contrastive Laplacian EigenmapsCode1
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive LearningCode1
Contrastive Learning for Representation Degeneration Problem in Sequential RecommendationCode1
Contrastive Learning-Based Audio to Lyrics Alignment for Multiple LanguagesCode1
Generate to Understand for RepresentationCode1
Contrastive Learning for Sports Video: Unsupervised Player ClassificationCode1
ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text EmbeddingsCode1
Generalized Few-Shot Continual Learning with Contrastive Mixture of AdaptersCode1
Multi-modal vision-language model for generalizable annotation-free pathology localization and clinical diagnosisCode1
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action RecognitionCode1
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean LabelCode1
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action RecognitionCode1
Temporal Context Aggregation for Video Retrieval with Contrastive LearningCode1
Bag of Instances Aggregation Boosts Self-supervised DistillationCode1
Contrastive Learning Is Spectral Clustering On Similarity GraphCode1
Context-self contrastive pretraining for crop type semantic segmentationCode1
A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning for Any Atlas and DisorderCode1
Contrastive Learning of Generalized Game RepresentationsCode1
Contrastive Learning of Sentence Embeddings from ScratchCode1
Contrastive Learning with Boosted MemorizationCode1
Balanced Contrastive Learning for Long-Tailed Visual RecognitionCode1
Contrastive Learning of Global-Local Video RepresentationsCode1
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with GeneticsCode1
Contrastive Learning of Musical RepresentationsCode1
Contrastive Learning of User Behavior Sequence for Context-Aware Document RankingCode1
Contrastive Learning with Bidirectional Transformers for Sequential RecommendationCode1
Contrastive Label Disambiguation for Partial Label LearningCode1
Graph Masked Autoencoder for Sequential RecommendationCode1
A Message Passing Perspective on Learning Dynamics of Contrastive LearningCode1
Contrastive Learning Reduces Hallucination in ConversationsCode1
From t-SNE to UMAP with contrastive learningCode1
Generalized Category Discovery with Large Language Models in the LoopCode1
Contrastive Learning with Cross-Modal Knowledge Mining for Multimodal Human Activity RecognitionCode1
Continuous Contrastive Learning for Long-Tailed Semi-Supervised RecognitionCode1
Continuous Learning for Android Malware DetectionCode1
ContraBAR: Contrastive Bayes-Adaptive Deep RLCode1
Contrastive Learning with Hard Negative Entities for Entity Set ExpansionCode1
ContraCLM: Contrastive Learning For Causal Language ModelCode1
BankNote-Net: Open dataset for assistive universal currency recognitionCode1
Contrastive Learning with Hard Negative SamplesCode1
ContraNorm: A Contrastive Learning Perspective on Oversmoothing and BeyondCode1
ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide SequencingCode1
BasisFormer: Attention-based Time Series Forecasting with Learnable and Interpretable BasisCode1
Contrastive Learning with Stronger AugmentationsCode1
Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive LearningCode1
Contrastive Masked Autoencoders are Stronger Vision LearnersCode1
Contrast and Classify: Training Robust VQA ModelsCode1
Graph Contrastive Learning for Anomaly DetectionCode1
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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