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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6601–6625 of 6661 papers

TitleStatusHype
Augmentation adversarial training for self-supervised speaker recognition—0
Hierarchical Contrastive Motion Learning for Video Action Recognition—0
GraphCL: Contrastive Self-Supervised Learning of Graph Representations—0
Vehicle Trajectory Prediction by Transfer Learning of Semi-Supervised Models—0
Representation Learning via Adversarially-Contrastive Optimal Transport—0
Contrastive Multiple Correspondence Analysis (cMCA): Using Contrastive Learning to Identify Latent Subgroups in Political PartiesCode0
SCE: Scalable Network Embedding from Sparsest CutCode0
Unsupervised Deep Representation Learning and Few-Shot Classification of PolSAR Images—0
Domain Contrast for Domain Adaptive Object Detection—0
Disentangle Perceptual Learning through Online Contrastive Learning—0
ContraGAN: Contrastive Learning for Conditional Image Generation—0
Unsupervised Image Classification for Deep Representation LearningCode0
Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images—0
Improved Conditional Flow Models for Molecule to Image SynthesisCode0
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels—0
Pairwise Supervision Can Provably Elicit a Decision Boundary—0
CSTNet: Contrastive Speech Translation Network for Self-Supervised Speech Representation Learning—0
Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning—0
On Mutual Information in Contrastive Learning for Visual Representations—0
What Makes for Good Views for Contrastive Learning?—0
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems—0
On Bottleneck Features for Text-Dependent Speaker Verification Using X-vectors—0
Distilling Localization for Self-Supervised Representation Learning—0
Clustering based Contrastive Learning for Improving Face Representations—0
Semi-supervised Contrastive Learning Using Partial Label Information—0
Show:102550
← PrevPage 265 of 267Next →
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