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 2951–2975 of 6661 papers

TitleStatusHype
Adults as Augmentations for Children in Facial Emotion Recognition with Contrastive Learning—0
Higher-order Cross-structural Embedding Model for Time Series Analysis—0
High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text-attributed Graphs—0
Fusion Self-supervised Learning for Recommendation—0
Investigating the Role of Negatives in Contrastive Representation Learning—0
Contrastive Learning of 3D Shape Descriptor with Dynamic Adversarial Views—0
Contrastive learning, multi-view redundancy, and linear models—0
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data—0
Generalized Class Discovery in Instance Segmentation—0
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction—0
Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement—0
Generalization Bounds for Adversarial Contrastive Learning—0
HNCSE: Advancing Sentence Embeddings via Hybrid Contrastive Learning with Hard Negatives—0
Hodge-Aware Contrastive Learning—0
Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learning—0
Homography augumented momentum constrastive learning for SAR image retrieval—0
Bootstrapping Vision-language Models for Self-supervised Remote Physiological Measurement—0
An Image-based Approach of Task-driven Driving Scene Categorization—0
Investigating Why Contrastive Learning Benefits Robustness Against Label Noise—0
How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?—0
Is Contrasting All You Need? Contrastive Learning for the Detection and Attribution of AI-generated Text—0
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?—0
Iterative Graph Self-Distillation—0
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning—0
Joint Debiased Representation and Image Clustering Learning with Self-Supervision—0
Show:102550
← PrevPage 119 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