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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 5551–5575 of 6661 papers

TitleStatusHype
CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning—0
Approximate Bayesian Computation via Classification—0
Exploring Feature Representation Learning for Semi-supervised Medical Image SegmentationCode0
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition—0
HoughCL: Finding Better Positive Pairs in Dense Self-supervised Learning—0
Decentralized Unsupervised Learning of Visual Representations—0
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive LearningCode1
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming—0
Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning—0
Combined Scaling for Zero-shot Transfer Learning—0
DeepQR: Neural-based Quality Ratings for Learnersourced Multiple-Choice Questions—0
CLMB: deep contrastive learning for robust metagenomic binningCode0
CSI: Contrastive Data Stratification for Interaction Prediction and its Application to Compound-Protein Interaction Prediction—0
The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled VideoCode1
SAPNet: Segmentation-Aware Progressive Network for Perceptual Contrastive DerainingCode1
Pedestrian Detection by Exemplar-Guided Contrastive Learning—0
Textual Entailment with Dynamic Contrastive Learning for Zero-shot NER—0
MDERank: A Masked Document Embedding Rank Approach for Unsupervised Keyphrase Extraction—0
Unsupervised Domain Adaptation with Contrastive Learning for Cross-domain Chinese NER—0
Repo4QA: Answering Complex Coding Questions via Dense Retrieval on GitHub Repositories—0
UNICON: Unsupervised Intent Discovery via Semantic-level Contrastive Learning—0
Improving Neural Topic Models by Contrastive Learning with BERT—0
Debiased Contrastive Learning of Unsupervised Sentence Representations—0
ReadE: Learning Relation-Dependent Entity Representation for Knowledge Graph Completion—0
A Unified and Efficient Contrastive Learning Framework for Text Summarization—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