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 3601–3625 of 6661 papers

TitleStatusHype
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning—0
A Chinese Spelling Check Framework Based on Reverse Contrastive Learning—0
ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation—0
A Classifier-Free Incremental Learning Framework for Scalable Medical Image Segmentation—0
A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images—0
A Closer Look at Few-shot Image Generation—0
A Comparative Analysis Of Latent Regressor Losses For Singing Voice Conversion—0
A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition—0
A comprehensive solution to retrieval-based chatbot construction—0
A Computational Account Of Self-Supervised Visual Learning From Egocentric Object Play—0
A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models—0
A Contrastive Framework for Learning Sentence Representations from Pairwise and Triple-wise Perspective in Angular Space—0
A contrastive learning approach for individual re-identification in a wild fish population—0
A contrastive-learning approach for auditory attention detection—0
A Contrastive Learning Approach to Auroral Identification and Classification—0
A Contrastive Learning Based Convolutional Neural Network for ERP Brain-Computer Interfaces—0
A Contrastive Learning Foundation Model Based on Perfectly Aligned Sample Pairs for Remote Sensing Images—0
A Co-training Approach for Noisy Time Series Learning—0
Acoustic identification of individual animals with hierarchical contrastive learning—0
A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning—0
A Cross-Corpus Speech Emotion Recognition Method Based on Supervised Contrastive Learning—0
Action-based Contrastive Learning for Trajectory Prediction—0
Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition—0
ACTIVE:Augmentation-Free Graph Contrastive Learning for Partial Multi-View Clustering—0
ActiveMatch: End-to-end Semi-supervised Active Representation Learning—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