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 4276–4300 of 6661 papers

TitleStatusHype
CvFormer: Cross-view transFormers with Pre-training for fMRI Analysis of Human Brain—0
Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax EntropyCode0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders—0
DebCSE: Rethinking Unsupervised Contrastive Sentence Embedding Learning in the Debiasing Perspective—0
Hodge-Aware Contrastive Learning—0
Instance Adaptive Prototypical Contrastive Embedding for Generalized Zero Shot Learning—0
Domain-Aware Augmentations for Unsupervised Online General Continual Learning—0
Multi-behavior Recommendation with SVD Graph Neural Networks—0
Grounded Language Acquisition From Object and Action Imagery—0
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language ModelCode0
Enhancing Hyperedge Prediction with Context-Aware Self-Supervised LearningCode0
ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space—0
Optimizing Audio Augmentations for Contrastive Learning of Health-Related Acoustic Signals—0
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition—0
Unified Contrastive Fusion Transformer for Multimodal Human Action Recognition—0
Latent Spatiotemporal Adaptation for Generalized Face Forgery Video Detection—0
Mask2Anomaly: Mask Transformer for Universal Open-set Segmentation—0
Unsupervised Gaze-aware Contrastive Learning with Subject-specific Condition—0
Label-efficient Contrastive Learning-based model for nuclei detection and classification in 3D Cardiovascular Immunofluorescent Images—0
Prompt-based Context- and Domain-aware Pretraining for Vision and Language Navigation—0
M(otion)-mode Based Prediction of Ejection Fraction using EchocardiogramsCode0
Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference—0
Exploring Semantic Consistency in Unpaired Image Translation to Generate Data for Surgical ApplicationsCode0
Contrastive Learning as Kernel Approximation—0
Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis ScreeningCode0
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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