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 5951–6000 of 6661 papers

TitleStatusHype
Token-Level Supervised Contrastive Learning for Punctuation RestorationCode1
Using system context information to complement weakly labeled data—0
Compound Figure Separation of Biomedical Images with Side LossCode0
OODformer: Out-Of-Distribution Detection TransformerCode1
Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus ImagesCode1
Self-supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation LossCode1
Contrastive Predictive Coding for Anomaly Detection—0
Towards an Interpretable Latent Space in Structured Models for Video Prediction—0
More Robust Dense Retrieval with Contrastive Dual LearningCode1
Multi-Level Contrastive Learning for Few-Shot Problems—0
Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based AttacksCode0
Generative Adversarial Learning via Kernel Density Discrimination—0
Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truthCode1
Contrastive Learning for Cold-Start RecommendationCode1
Segmentation of VHR EO Images using Unsupervised Learning—0
3D Neural Scene Representations for Visuomotor Control—0
Staying in Shape: Learning Invariant Shape Representations using Contrastive Learning—0
Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis—0
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation—0
Improving Text-to-Image Synthesis Using Contrastive LearningCode1
A Hierarchical Dual Model of Environment- and Place-Specific Utility for Visual Place RecognitionCode1
Multi-Level Graph Contrastive Learning—0
Contrastive Multimodal Fusion with TupleInfoNCECode1
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning—0
Continual Contrastive Learning for Image ClassificationCode0
Distance-based Hyperspherical Classification for Multi-source Open-Set Domain AdaptationCode1
Bag of Instances Aggregation Boosts Self-supervised DistillationCode1
Supervised Contrastive Learning for Accented Speech Recognition—0
Inter-intra Variant Dual Representations forSelf-supervised Video RecognitionCode0
Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCECode1
Blind Image Super-Resolution via Contrastive Representation Learning—0
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language UnderstandingCode1
CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation—0
Exploring Localization for Self-supervised Fine-grained Contrastive Learning—0
Multi-Source domain adaptation via supervised contrastive learning and confident consistency regularization—0
OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled DataCode1
Interactive Dimensionality Reduction for Comparative AnalysisCode0
Few-Shot Electronic Health Record Coding through Graph Contrastive LearningCode0
SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption—0
Self-Contrastive Learning: Single-viewed Supervised Contrastive Framework using Sub-networkCode1
Co^2L: Contrastive Continual LearningCode1
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning—0
Time-Series Representation Learning via Temporal and Contextual ContrastingCode1
UMIC: An Unreferenced Metric for Image Captioning via Contrastive LearningCode1
On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy—0
Crossmodal clustered contrastive learning: Grounding of spoken language to gestureCode0
Winning the CVPR'2021 Kinetics-GEBD Challenge: Contrastive Learning ApproachCode1
VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive LearningCode1
Can contrastive learning avoid shortcut solutions?Code1
Multi-level Feature Learning for Contrastive Multi-view ClusteringCode1
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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