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 2701–2725 of 6661 papers

TitleStatusHype
Multi-perspective Contrastive Logit Distillation—0
Masked Image Contrastive Learning for Efficient Visual Conceptual Pre-training—0
Layer Importance and Hallucination Analysis in Large Language Models via Enhanced Activation Variance-Sparsity—0
DR-BFR: Degradation Representation with Diffusion Models for Blind Face Restoration—0
Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications—0
PFML: Self-Supervised Learning of Time-Series Data Without Representation CollapseCode0
Leveraging large language models for efficient representation learning for entity resolution—0
Less is More: Unseen Domain Fake News Detection via Causal Propagation Substructures—0
Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction—0
A Self-Supervised Model for Multi-modal Stroke Risk PredictionCode0
Partial Multi-View Clustering via Meta-Learning and Contrastive Feature Alignment—0
Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI Representations for Decoding, Encoding, and Modality Conversion—0
Scale Contrastive Learning with Selective Attentions for Blind Image Quality Assessment—0
CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision—0
Aligning Visual Contrastive learning models via Preference OptimizationCode0
Prompt-enhanced Network for Hateful Meme ClassificationCode0
MoCoKGC: Momentum Contrast Entity Encoding for Knowledge Graph Completion—0
SynCL: A Synergistic Training Strategy with Instance-Aware Contrastive Learning for End-to-End Multi-Camera 3D Tracking—0
Decoding Visual Experience and Mapping Semantics through Whole-Brain Analysis Using fMRI Foundation ModelsCode0
Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering—0
Variational Graph Contrastive LearningCode0
Finding "Good Views" of Electrocardiogram Signals for Inferring Abnormalities in Cardiac ConditionCode0
Learning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation—0
Model Editing for LLMs4Code: How Far are We?Code0
Understanding the Role of Equivariance in Self-supervised LearningCode0
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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