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 65516575 of 6661 papers

TitleStatusHype
InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-TrainingCode1
Vehicle Trajectory Prediction by Transfer Learning of Semi-Supervised Models0
Representation Learning via Adversarially-Contrastive Optimal Transport0
Contrastive Code Representation LearningCode1
InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive LearningCode1
Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political PartiesCode0
Improving Weakly Supervised Visual Grounding by Contrastive Knowledge DistillationCode1
Data Augmenting Contrastive Learning of Speech Representations in the Time DomainCode1
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text RetrievalCode1
Debiased Contrastive LearningCode1
Subject-Aware Contrastive Learning for BiosignalsCode1
SCE: Scalable Network Embedding from Sparsest CutCode0
Video Representation Learning with Visual Tempo ConsistencyCode1
Unsupervised Deep Representation Learning and Few-Shot Classification of PolSAR Images0
On Equivariant and Invariant Learning of Object Landmark RepresentationsCode1
Domain Contrast for Domain Adaptive Object Detection0
Disentangle Perceptual Learning through Online Contrastive Learning0
ContraGAN: Contrastive Learning for Conditional Image Generation0
Unsupervised Image Classification for Deep Representation LearningCode0
Supervision Accelerates Pre-training in Contrastive Semi-Supervised Learning of Visual RepresentationsCode1
Joint Contrastive Learning for Unsupervised Domain AdaptationCode1
Contrastive learning of global and local features for medical image segmentation with limited annotationsCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
Unsupervised Learning of Visual Features by Contrasting Cluster AssignmentsCode2
Contrastive Learning for Weakly Supervised Phrase GroundingCode1
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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