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 6226–6250 of 6661 papers

TitleStatusHype
Supervised Contrastive Learning for Interpretable Long-Form Document MatchingCode0
Group-based Distinctive Image Captioning with Memory Attention—0
Self-Supervised Video Representation Learning with Meta-Contrastive Network—0
ALBRT: Cellular Composition Prediction in Routine Histology ImagesCode0
Self-Supervised Pretraining and Controlled Augmentation Improve Rare Wildlife Recognition in UAV ImagesCode0
Unsupervised Person Re-identification with Stochastic Training StrategyCode0
Deep Contrastive Multiview Network Embedding—0
Self-supervised Contrastive Learning of Multi-view Facial Expressions—0
Unsupervised Disentanglement without Autoencoding: Pitfalls and Future DirectionsCode0
Collaborative Unsupervised Visual Representation Learning from Decentralized DataCode0
GeoCLR: Georeference Contrastive Learning for Efficient Seafloor Image Interpretation—0
Self-supervised Contrastive Learning for Irrigation Detection in Satellite ImageryCode0
Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound—0
Learning Oculomotor Behaviors from ScanpathCode0
Few-Shot Segmentation with Global and Local Contrastive LearningCode0
Cervical Optical Coherence Tomography Image Classification Based on Contrastive Self-Supervised Texture LearningCode0
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation—0
Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translation—0
The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data—0
Rapid Automated Analysis of Skull Base Tumor Specimens Using Intraoperative Optical Imaging and Artificial Intelligence—0
Triplet Contrastive Learning for Brain Tumor Classification—0
Towards Discriminative Representation Learning for Unsupervised Person Re-identification—0
Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation—0
OSCAR-Net: Object-centric Scene Graph Attention for Image Attribution—0
Contrastive Learning for View Classification of Echocardiograms—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