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 6276–6300 of 6661 papers

TitleStatusHype
Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based AttacksCode0
Generative Adversarial Learning via Kernel Density Discrimination—0
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
Multi-Level Graph Contrastive Learning—0
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation—0
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning—0
Continual Contrastive Learning for Image ClassificationCode0
Supervised Contrastive Learning for Accented Speech Recognition—0
Inter-intra Variant Dual Representations forSelf-supervised Video RecognitionCode0
Blind Image Super-Resolution via Contrastive Representation Learning—0
CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation—0
Multi-Source domain adaptation via supervised contrastive learning and confident consistency regularization—0
Exploring Localization for Self-supervised Fine-grained Contrastive Learning—0
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
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning—0
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
Interventional Video Grounding with Dual Contrastive LearningCode0
Practical Assessment of Generalization Performance Robustness for Deep Networks via Contrastive Examples—0
Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images—0
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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