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 3801–3825 of 6661 papers

TitleStatusHype
GaussianStyle: Gaussian Head Avatar via StyleGANCode0
Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive LearningCode0
Learning Label Hierarchy with Supervised Contrastive LearningCode0
iMove: Exploring Bio-impedance Sensing for Fitness Activity Recognition—0
Rank Supervised Contrastive Learning for Time Series Classification—0
Episodic-free Task Selection for Few-shot Learning—0
Graph Multi-Similarity Learning for Molecular Property Prediction—0
Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation—0
All Beings Are Equal in Open Set Recognition—0
Optimizing contrastive learning for cortical folding pattern detectionCode0
Morality is Non-Binary: Building a Pluralist Moral Sentence Embedding Space using Contrastive LearningCode0
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker JointsCode0
Detection and Recovery Against Deep Neural Network Fault Injection Attacks Based on Contrastive Learning—0
Self-Supervised Representation Learning for Nerve Fiber Distribution Patterns in 3D-PLI—0
PICL: Physics Informed Contrastive Learning for Partial Differential EquationsCode0
Regressing Transformers for Data-efficient Visual Place Recognition—0
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling—0
MLEM: Generative and Contrastive Learning as Distinct Modalities for Event SequencesCode0
ConFit: Improving Resume-Job Matching using Data Augmentation and Contrastive Learning—0
Transformer-based Clipped Contrastive Quantization Learning for Unsupervised Image Retrieval—0
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective—0
Incorporating simulated spatial context information improves the effectiveness of contrastive learning models—0
Challenging Low Homophily in Social Recommendation—0
PepGB: Facilitating peptide drug discovery via graph neural networks—0
Improving Fairness of Automated Chest X-ray Diagnosis by Contrastive LearningCode0
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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