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 Deep Nonnegative Matrix Factorization for Community DetectionCode1
CONTaiNER: Few-Shot Named Entity Recognition via Contrastive LearningCode1
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
Cross-modal Contrastive Learning for Speech TranslationCode1
Cross-Silo Prototypical Calibration for Federated Learning with Non-IID DataCode1
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Deep Graph Contrastive Representation LearningCode1
Deep Multiview Clustering by Contrasting Cluster AssignmentsCode1
Data Augmentation-free Unsupervised Learning for 3D Point Cloud UnderstandingCode1
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-ResolutionCode1
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean LabelCode1
Delving StyleGAN Inversion for Image Editing: A Foundation Latent Space ViewpointCode1
CrossCBR: Cross-view Contrastive Learning for Bundle RecommendationCode1
Bag of Instances Aggregation Boosts Self-supervised DistillationCode1
Denoising-Aware Contrastive Learning for Noisy Time SeriesCode1
Context-self contrastive pretraining for crop type semantic segmentationCode1
Automatic Biomedical Term Clustering by Learning Fine-grained Term RepresentationsCode1
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive AlignmentCode1
CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal GroundingCode1
Conditioned and Composed Image Retrieval Combining and Partially Fine-Tuning CLIP-Based FeaturesCode1
Balanced Contrastive Learning for Long-Tailed Visual RecognitionCode1
Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive LearningCode1
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with GeneticsCode1
Diagnosing and Rectifying Vision Models using LanguageCode1
Differentiable Data Augmentation for Contrastive Sentence Representation LearningCode1
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse GranularityCode1
Diffusion-based Contrastive Learning for Sequential RecommendationCode1
Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual LearningCode1
A Message Passing Perspective on Learning Dynamics of Contrastive LearningCode1
Extending global-local view alignment for self-supervised learning with remote sensing imageryCode1
Direct Preference-based Policy Optimization without Reward ModelingCode1
DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP TrainingCode1
Contrastive Label Disambiguation for Partial Label LearningCode1
Continuous Contrastive Learning for Long-Tailed Semi-Supervised RecognitionCode1
Continuous Learning for Android Malware DetectionCode1
ContraBAR: Contrastive Bayes-Adaptive Deep RLCode1
Contrastive Grouping with Transformer for Referring Image SegmentationCode1
ContraCLM: Contrastive Learning For Causal Language ModelCode1
BankNote-Net: Open dataset for assistive universal currency recognitionCode1
Disentangled Causal Embedding With Contrastive Learning For Recommender SystemCode1
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
Dissolving Is Amplifying: Towards Fine-Grained Anomaly DetectionCode1
Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information MaximizationCode1
Distilling Visual Priors from Self-Supervised LearningCode1
Contrast and Classify: Training Robust VQA ModelsCode1
CRIS: CLIP-Driven Referring Image SegmentationCode1
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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