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 6151–6175 of 6661 papers

TitleStatusHype
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
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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