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 901–950 of 6661 papers

TitleStatusHype
User Retention-oriented Recommendation with Decision TransformerCode1
FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual and Cross-Model Social Bot DetectionCode1
Adaptive Supervised PatchNCE Loss for Learning H&E-to-IHC Stain Translation with Inconsistent Groundtruth Image PairsCode1
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive LearningCode1
Convolutional Cross-View Pose EstimationCode1
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic SegmentationCode1
A Message Passing Perspective on Learning Dynamics of Contrastive LearningCode1
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive LearningCode1
Learning Efficient Coding of Natural Images with Maximum Manifold Capacity RepresentationsCode1
CoRTX: Contrastive Framework for Real-time ExplanationCode1
Rethinking the Effect of Data Augmentation in Adversarial Contrastive LearningCode1
Heterogeneous Graph Contrastive Learning for RecommendationCode1
Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive LearningCode1
Dissolving Is Amplifying: Towards Fine-Grained Anomaly DetectionCode1
Contrastive Video Question Answering via Video Graph TransformerCode1
Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient RepresentationsCode1
Knowledge-infused Contrastive Learning for Urban Imagery-based Socioeconomic PredictionCode1
Cross-modal Contrastive Learning for Multimodal Fake News DetectionCode1
Learning Visual Representations via Language-Guided SamplingCode1
Test-Time Distribution Normalization for Contrastively Learned Vision-language ModelsCode1
Cross-Modal Retrieval with Partially Mismatched PairsCode1
Data-Efficient Contrastive Self-supervised Learning: Most Beneficial Examples for Supervised Learning Contribute the LeastCode1
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton SequencesCode1
Like a Good Nearest Neighbor: Practical Content Moderation and Text ClassificationCode1
CluCDD:Contrastive Dialogue Disentanglement via ClusteringCode1
Multi-Source Contrastive Learning from Musical AudioCode1
CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D DatasetsCode1
Type-Aware Decomposed Framework for Few-Shot Named Entity RecognitionCode1
Generalized Few-Shot Continual Learning with Contrastive Mixture of AdaptersCode1
LipLearner: Customizable Silent Speech Interactions on Mobile DevicesCode1
Compositional Exemplars for In-context LearningCode1
Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image AnalysisCode1
Self-Supervised Node Representation Learning via Node-to-Neighbourhood AlignmentCode1
Diagnosing and Rectifying Vision Models using LanguageCode1
Continuous Learning for Android Malware DetectionCode1
Disentangled Causal Embedding With Contrastive Learning For Recommender SystemCode1
Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining ApproachCode1
Cluster-Level Contrastive Learning for Emotion Recognition in ConversationsCode1
Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous InputsCode1
Contrastive Collaborative Filtering for Cold-Start Item RecommendationCode1
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction PerspectiveCode1
A latent space for unsupervised MR image quality control via artifact assessmentCode1
Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural NetworksCode1
ZhichunRoad at Amazon KDD Cup 2022: MultiTask Pre-Training for E-Commerce Product SearchCode1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World AttacksCode1
GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait RecognitionCode1
Direct Preference-based Policy Optimization without Reward ModelingCode1
Mutual Wasserstein Discrepancy Minimization for Sequential RecommendationCode1
Incomplete Multi-view Clustering via Prototype-based ImputationCode1
Graph Contrastive Learning for Skeleton-based Action RecognitionCode1
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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