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 1701–1725 of 6661 papers

TitleStatusHype
Self-Contrastive Graph Diffusion NetworkCode1
Data Poisoning Attacks Against Multimodal EncodersCode1
Data Poisoning based Backdoor Attacks to Contrastive LearningCode1
Dynamic Clustering and Cluster Contrastive Learning for Unsupervised Person Re-identificationCode1
End-to-end training of Multimodal Model and ranking ModelCode1
Self-labelling via simultaneous clustering and representation learningCode1
Community-Invariant Graph Contrastive LearningCode1
Empowering Collaborative Filtering with Principled Adversarial Contrastive LossCode1
PeCLR: Self-Supervised 3D Hand Pose Estimation from monocular RGB via Equivariant Contrastive LearningCode1
Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton SequencesCode1
Dynamic Contrastive Knowledge Distillation for Efficient Image RestorationCode1
Self-Supervised Contrastive Learning for Unsupervised Phoneme SegmentationCode1
Self-Supervised Contrastive Learning for Singing VoicesCode1
DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing ModalitiesCode1
Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report GenerationCode1
Energy-Based Contrastive Learning of Visual RepresentationsCode1
Self-Supervised Graph Co-Training for Session-based RecommendationCode1
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical ImagingCode1
Self-supervised Heterogeneous Graph Neural Network with Co-contrastive LearningCode1
Self-Supervised Learning Disentangled Group Representation as FeatureCode1
Debiased Contrastive LearningCode1
Debiased Contrastive Learning for Sequential RecommendationCode1
CLCC: Contrastive Learning for Color ConstancyCode1
Self-Supervised Learning from Automatically Separated Sound ScenesCode1
Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset and Comprehensive FrameworkCode1
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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