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 6551–6575 of 6661 papers

TitleStatusHype
Momentum Contrast Speaker Representation Learning—0
Contrastive Self-Supervised Learning for Wireless Power ControlCode0
Self-Supervised Contrastive Learning for Efficient User Satisfaction Prediction in Conversational Agents—0
Contrastive Learning of General-Purpose Audio RepresentationsCode0
CLAR: Contrastive Learning of Auditory Representations—0
Less can be more in contrastive learning—0
MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patientsCode0
Unsupervised Natural Language Inference via Decoupled Multimodal Contrastive LearningCode0
Fully Unsupervised Person Re-identification viaSelective Contrastive Learning—0
Self-Supervised Ranking for Representation Learning—0
Function Contrastive Learning of Transferable Meta-Representations—0
An Analysis of Robustness of Non-Lipschitz NetworksCode0
Contrastive Rendering for Ultrasound Image Segmentation—0
Contrastive Representation Learning: A Framework and Review—0
MMGSD: Multi-Modal Gaussian Shape Descriptors for Correspondence Matching in 1D and 2D Deformable Objects—0
A Contrastive Learning Approach for Training Variational Autoencoder Priors—0
Support-set bottlenecks for video-text representation learning—0
A Simple Framework for Uncertainty in Contrastive Learning—0
CO2: Consistent Contrast for Unsupervised Visual Representation Learning—0
EqCo: Equivalent Rules for Self-supervised Contrastive LearningCode0
Conditional Negative Sampling for Contrastive Learning of Visual RepresentationsCode0
CoKe: Localized Contrastive Learning for Robust Keypoint Detection—0
Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive LearningCode0
DROPS: Deep Retrieval of Physiological Signals via Attribute-specific Clinical Prototypes—0
What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator—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