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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4401–4450 of 6661 papers

TitleStatusHype
Learning beyond sensations: how dreams organize neuronal representations—0
COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery DetectionCode0
ADS-Cap: A Framework for Accurate and Diverse Stylized Captioning with Unpaired Stylistic CorporaCode0
ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders—0
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach—0
Three Factors to Improve Out-of-Distribution Detection—0
Orientation-Guided Contrastive Learning for UAV-View Geo-Localisation—0
A Multi-Source Data Fusion-based Semantic Segmentation Model for Relic Landslide Detection—0
Dynamically Scaled Temperature in Self-Supervised Contrastive LearningCode0
Graph Contrastive Learning with Generative Adversarial Network—0
Center Contrastive Loss for Metric Learning—0
Graph Embedding Dynamic Feature-based Supervised Contrastive Learning of Transient Stability for Changing Power Grid Topologies—0
Self-Supervised Contrastive BERT Fine-tuning for Fusion-based Reviewed-Item RetrievalCode0
Contrastive Learning for API Aspect AnalysisCode0
Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?Code0
Contrastive Conditional Latent Diffusion for Audio-visual SegmentationCode0
C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation—0
Self-Supervised Learning of Gait-Based Biomarkers—0
Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images—0
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs—0
BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection—0
Self-Supervised Graph Transformer for Deepfake Detection—0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
G2L: Semantically Aligned and Uniform Video Grounding via Geodesic and Game Theory—0
Towards multi-modal anatomical landmark detection for ultrasound-guided brain tumor resection with contrastive learning—0
Gaussian Graph with Prototypical Contrastive Learning in E-Commerce Bundle Recommendation—0
Learning Dense Correspondences between Photos and Sketches—0
DeepGATGO: A Hierarchical Pretraining-Based Graph-Attention Model for Automatic Protein Function Prediction—0
Homophily-Driven Sanitation View for Robust Graph Contrastive Learning—0
Rule By Example: Harnessing Logical Rules for Explainable Hate Speech DetectionCode0
Phase Matching for Out-of-Distribution Generalization—0
Towards a Visual-Language Foundation Model for Computational Pathology—0
General-Purpose Multi-Modal OOD Detection Framework—0
Hallucination Improves the Performance of Unsupervised Visual Representation Learning—0
Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning—0
Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG SignalCode0
Distribution Shift Matters for Knowledge Distillation with Webly Collected Images—0
Learning Discriminative Visual-Text Representation for Polyp Re-IdentificationCode0
Extreme Multi-Label Skill Extraction Training using Large Language Models—0
Language-Enhanced Session-Based Recommendation with Decoupled Contrastive Learning—0
Identical and Fraternal Twins: Fine-Grained Semantic Contrastive Learning of Sentence Representations—0
Space Engage: Collaborative Space Supervision for Contrastive-based Semi-Supervised Semantic Segmentation—0
GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction—0
Towards the Sparseness of Projection Head in Self-Supervised Learning—0
Contrastive Multi-Task Dense Prediction—0
Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning—0
Semantic Contrastive Bootstrapping for Single-positive Multi-label RecognitionCode0
RegExplainer: Generating Explanations for Graph Neural Networks in Regression TasksCode0
AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge—0
Cross-Model Cross-Stream Learning for Self-Supervised Human Action RecognitionCode0
Show:102550
← PrevPage 89 of 134Next →
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