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 5126–5150 of 6661 papers

TitleStatusHype
Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images—0
Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder—0
Masked Reconstruction Contrastive Learning with Information Bottleneck Principle—0
Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment PredictionCode0
Unveiling interpretable development-specific gene signatures in the developing human prefrontal cortex with ICGSCode0
Region Embedding with Intra and Inter-View Contrastive LearningCode0
Unsupervised Feature Clustering Improves Contrastive Representation Learning for Medical Image Segmentation—0
DeepRGVP: A Novel Microstructure-Informed Supervised Contrastive Learning Framework for Automated Identification Of The Retinogeniculate Pathway Using dMRI Tractography—0
The Role of Local Alignment and Uniformity in Image-Text Contrastive Learning on Medical Images—0
An online algorithm for contrastive Principal Component Analysis—0
ContextCLIP: Contextual Alignment of Image-Text pairs on CLIP visual representations—0
Imagination is All You Need! Curved Contrastive Learning for Abstract Sequence Modeling Utilized on Long Short-Term Dialogue PlanningCode0
TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction—0
Information-guided pixel augmentation for pixel-wise contrastive learning—0
SCOTCH and SODA: A Transformer Video Shadow Detection Framework—0
Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learning—0
3D-Aware Encoding for Style-based Neural Radiance Fields—0
Federated Unsupervised Visual Representation Learning via Exploiting General Content and Personal Style—0
Masked Contrastive Representation Learning—0
Cross-Platform and Cross-Domain Abusive Language Detection with Supervised Contrastive Learning—0
Mitigating Forgetting in Online Continual Learning via Contrasting Semantically Distinct Augmentations—0
Contrastive Learning for Climate Model Bias Correction and Super-Resolution—0
Equivariant Contrastive Learning for Sequential RecommendationCode0
Self-supervised learning of audio representations using angular contrastive lossCode0
Clinical Contrastive Learning for Biomarker Detection—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