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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 4351–4400 of 6661 papers

TitleStatusHype
ReConTab: Regularized Contrastive Representation Learning for Tabular Data—0
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification—0
Reducing Distraction in Long-Context Language Models by Focused Learning—0
Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach—0
Refine Knowledge of Large Language Models via Adaptive Contrastive Learning—0
RefineVIS: Video Instance Segmentation with Temporal Attention Refinement—0
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks—0
Refining Self-Supervised Learning in Imaging: Beyond Linear Metric—0
Region-aware Knowledge Distillation for Efficient Image-to-Image Translation—0
Region-based Contrastive Pretraining for Medical Image Retrieval with Anatomic Query—0
Registering Neural Radiance Fields as 3D Density Images—0
Regressing Transformers for Data-efficient Visual Place Recognition—0
Regularized Contrastive Learning of Semantic Search—0
Regularized Contrastive Partial Multi-view Outlier Detection—0
Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives—0
Rehearsal-free Federated Domain-incremental Learning—0
Reinforced Interactive Continual Learning via Real-time Noisy Human Feedback—0
Relational Representation Learning in Visually-Rich Documents—0
Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure—0
Relation-aware graph structure embedding with co-contrastive learning for drug-drug interaction prediction—0
Relation-based Counterfactual Data Augmentation and Contrastive Learning for Robustifying Natural Language Inference Models—0
Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction—0
Relation Modeling and Distillation for Learning with Noisy Labels—0
Relative Counterfactual Contrastive Learning for Mitigating Pretrained Stance Bias in Stance Detection—0
Relative distance matters for one-shot landmark detection—0
RelTopo: Enhancing Relational Modeling for Driving Scene Topology Reasoning—0
Remember the Difference: Cross-Domain Few-Shot Semantic Segmentation via Meta-Memory Transfer—0
Remote Heart Rate Monitoring in Smart Environments from Videos with Self-supervised Pre-training—0
Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images—0
Replace-then-Perturb: Targeted Adversarial Attacks With Visual Reasoning for Vision-Language Models—0
Repo4QA: Answering Complex Coding Questions via Dense Retrieval on GitHub Repositories—0
RePre: Improving Self-Supervised Vision Transformer with Reconstructive Pre-training—0
Sentence-aware Contrastive Learning for Open-Domain Passage Retrieval—0
Representation Disentanglement in Generative Models with Contrastive Learning—0
Representation Learning on Out of Distribution in Tabular Data—0
Representation Learning via Adversarially-Contrastive Optimal Transport—0
Representation of perceived prosodic similarity of conversational feedback—0
Representative Image Feature Extraction via Contrastive Learning Pretraining for Chest X-ray Report Generation—0
RepsNet: Combining Vision with Language for Automated Medical Reports—0
An Effective Deployment of Contrastive Learning in Multi-label Text Classification—0
Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Residual Contrastive Learning: Unsupervised Representation Learning from Residuals—0
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models—0
Resolving Sentiment Discrepancy for Multimodal Sentiment Detection via Semantics Completion and Decomposition—0
Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning—0
REST: REtrieve & Self-Train for generative action recognition—0
RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning—0
Rethink Arbitrary Style Transfer with Transformer and Contrastive 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