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 3451–3475 of 6661 papers

TitleStatusHype
UDUC: An Uncertainty-driven Approach for Learning-based Robust Control—0
Sign-Guided Bipartite Graph Hashing for Hamming Space Search—0
SoftMCL: Soft Momentum Contrastive Learning for Fine-grained Sentiment-aware Pre-trainingCode0
A Mutual Information Perspective on Federated Contrastive Learning—0
Enhancing Micro Gesture Recognition for Emotion Understanding via Context-aware Visual-Text Contrastive LearningCode0
Goal-conditioned reinforcement learning for ultrasound navigation guidance—0
SoMeR: Multi-View User Representation Learning for Social Media—0
Feature-Aware Noise Contrastive Learning for Unsupervised Red Panda Re-Identification—0
A Self-explaining Neural Architecture for Generalizable Concept LearningCode0
Block-As-Domain Adaptation for Workload Prediction from fNIRS Data—0
Weighted Point Cloud Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric—0
Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning—0
StablePT: Towards Stable Prompting for Few-shot Learning via Input SeparationCode0
SemiPL: A Semi-supervised Method for Event Sound Source LocalizationCode0
Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for HistologyCode0
ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference OptimizationCode0
Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement—0
Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum—0
A Hybrid Approach for Document Layout Analysis in Document images—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
PromptCL: Improving Event Representation via Prompt Template and Contrastive LearningCode0
Tabular Data Contrastive Learning via Class-Conditioned and Feature-Correlation Based AugmentationCode0
A Unified Label-Aware Contrastive Learning Framework for Few-Shot Named Entity Recognition—0
2M-NER: Contrastive Learning for Multilingual and Multimodal NER with Language and Modal Fusion—0
ConKeD++ -- Improving descriptor learning for retinal image registration: A comprehensive study of contrastive losses—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