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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 3801–3825 of 6661 papers

TitleStatusHype
A Survey on Data Curation for Visual Contrastive Learning: Why Crafting Effective Positive and Negative Pairs Matters—0
A Survey on Masked Autoencoder for Self-supervised Learning in Vision and Beyond—0
A Survey on Self-supervised Contrastive Learning for Multimodal Text-Image Analysis—0
AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction—0
A Tale of Two Classes: Adapting Supervised Contrastive Learning to Binary Imbalanced Datasets—0
A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches—0
Athena: Safe Autonomous Agents with Verbal Contrastive Learning—0
A Theoretical Analysis of Contrastive Unsupervised Representation Learning—0
A theoretical framework for self-supervised contrastive learning for continuous dependent data—0
A Theoretical Study of Inductive Biases in Contrastive Learning—0
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning—0
ATM: Action Temporality Modeling for Video Question Answering—0
ATM-Net: Anatomy-Aware Text-Guided Multi-Modal Fusion for Fine-Grained Lumbar Spine Segmentation—0
A Topic-aware Summarization Framework with Different Modal Side Information—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition—0
Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity—0
Attention-based Interactive Disentangling Network for Instance-level Emotional Voice Conversion—0
Attention-guided Multi-step Fusion: A Hierarchical Fusion Network for Multimodal Recommendation—0
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation—0
Attention Mechanism for Contrastive Learning in GAN-based Image-to-Image Translation—0
Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking—0
Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce—0
Attention-wise masked graph contrastive learning for predicting molecular property—0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—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