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 2726–2750 of 6661 papers

TitleStatusHype
PRISM: Privacy-preserving Inter-Site MRI Harmonization via Disentangled Representation LearningCode0
Personalized News Recommendation System via LLM Embedding and Co-Occurrence Patterns—0
Reducing Distraction in Long-Context Language Models by Focused Learning—0
Predicting Stroke through Retinal Graphs and Multimodal Self-supervised LearningCode0
Enhancing Cardiovascular Disease Prediction through Multi-Modal Self-Supervised LearningCode0
ImpScore: A Learnable Metric For Quantifying The Implicitness Level of LanguageCode0
ACCIO: Table Understanding Enhanced via Contrastive Learning with AggregationsCode0
Judge Like a Real Doctor: Dual Teacher Sample Consistency Framework for Semi-supervised Medical Image Classification—0
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing—0
On the Comparison between Multi-modal and Single-modal Contrastive Learning—0
Understanding Contrastive Learning via Gaussian Mixture Models—0
Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud RegistrationCode0
FPPL: An Efficient and Non-IID Robust Federated Continual Learning FrameworkCode0
Fine Grained Insider Risk Detection—0
Exploring Optimal Transport-Based Multi-Grained Alignments for Text-Molecule Retrieval—0
Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional NetworksCode0
DPCL-Diff: The Temporal Knowledge Graph Reasoning Based on Graph Node Diffusion Model with Dual-Domain Periodic Contrastive Learning—0
Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination—0
Learning Hidden Subgoals under Temporal Ordering Constraints in Reinforcement Learning—0
OSAD: Open-Set Aircraft Detection in SAR Images—0
LEARNER: Learning Granular Labels from Coarse Labels using Contrastive Learning—0
Multi-Channel Hypergraph Contrastive Learning for Matrix Completion—0
Negative-Free Self-Supervised Gaussian Embedding of GraphsCode0
Replace-then-Perturb: Targeted Adversarial Attacks With Visual Reasoning for Vision-Language Models—0
How to Bridge Spatial and Temporal Heterogeneity in Link Prediction? A Contrastive Method—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