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 4876–4900 of 6661 papers

TitleStatusHype
Generalization Analysis for Contrastive Representation Learning—0
Attention Mechanism for Contrastive Learning in GAN-based Image-to-Image Translation—0
Novel Class Discovery: an Introduction and Key ConceptsCode0
DMMG: Dual Min-Max Games for Self-Supervised Skeleton-Based Action Recognition—0
Saliency Guided Contrastive Learning on Scene Images—0
A Global and Patch-wise Contrastive Loss for Accurate Automated Exudate DetectionCode0
ACE: Zero-Shot Image to Image Translation via Pretrained Auto-Contrastive-EncoderCode0
K-Diag: Knowledge-enhanced Disease Diagnosis in Radiographic Imaging—0
Contrastive Representation Learning for Acoustic Parameter Estimation—0
Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning—0
Mask-guided BERT for Few Shot Text Classification—0
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention—0
DrasCLR: A Self-supervised Framework of Learning Disease-related and Anatomy-specific Representation for 3D Medical Images—0
A General-Purpose Transferable Predictor for Neural Architecture Search—0
Unpaired Translation from Semantic Label Maps to Images by Leveraging Domain-Specific Simulations—0
Generalization Bounds for Adversarial Contrastive Learning—0
Heterogeneous Social Event Detection via Hyperbolic Graph RepresentationsCode0
Supervised Contrastive Learning and Feature Fusion for Improved Kinship Verification—0
Pseudo Contrastive Learning for Graph-based Semi-supervised Learning—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks—0
EnfoMax: Domain Entropy and Mutual Information Maximization for Domain Generalized Face Anti-spoofing—0
LabelPrompt: Effective Prompt-based Learning for Relation Classification—0
Bridge the Gap between Language models and Tabular Understanding—0
Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking—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