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

Showing 46764700 of 6661 papers

TitleStatusHype
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
Multimodal SuperCon: Classifier for Drivers of Deforestation in IndonesiaCode1
Knowing Where and What: Unified Word Block Pretraining for Document UnderstandingCode0
Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning0
Time to augment self-supervised visual representation learning0
Optimizing transformations for contrastive learning in a differentiable framework0
Contrastive Masked Autoencoders are Stronger Vision LearnersCode1
Semi-supervised 3D Object Detection with Proficient TeachersCode1
Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative MiningCode1
Criteria Comparative Learning for Real-scene Image Super-ResolutionCode0
Contrastive Learning for Interactive Recommendation in Fashion0
Generative Subgraph Contrast for Self-Supervised Graph Representation LearningCode1
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning0
Online Continual Learning with Contrastive Vision Transformer0
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly DetectionCode0
Deep Pneumonia: Attention-Based Contrastive Learning for Class-Imbalanced Pneumonia Lesion Recognition in Chest X-rays0
Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosisCode1
Online Knowledge Distillation via Mutual Contrastive Learning for Visual RecognitionCode1
Bi-directional Contrastive Learning for Domain Adaptive Semantic Segmentation0
Adaptive Soft Contrastive LearningCode1
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial RobustnessCode1
In Defense of Online Models for Video Instance SegmentationCode2
Correspondence Matters for Video Referring Expression ComprehensionCode1
Feature Representation Learning for Unsupervised Cross-domain Image RetrievalCode1
Hierarchically Self-Supervised Transformer for Human Skeleton Representation LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50ImageNet Top-1 Accuracy73.6Unverified
2ResNet50ImageNet Top-1 Accuracy73Unverified
3ResNet50ImageNet Top-1 Accuracy71.1Unverified
4ResNet50ImageNet Top-1 Accuracy69.3Unverified
5ResNet50 (v2)ImageNet Top-1 Accuracy67.6Unverified
6ResNet50 (v2)ImageNet Top-1 Accuracy63.8Unverified
7ResNet50ImageNet Top-1 Accuracy63.6Unverified
8ResNet50ImageNet Top-1 Accuracy61.5Unverified
9ResNet50ImageNet Top-1 Accuracy61.5Unverified
10ResNet50 (4×)ImageNet Top-1 Accuracy61.3Unverified
#ModelMetricClaimedVerifiedStatus
110..5sec1Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)84.77Unverified
#ModelMetricClaimedVerifiedStatus
1IPCL (ResNet18)Accuracy (Top-1)85.55Unverified