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 3001–3050 of 6661 papers

TitleStatusHype
Generalization Beyond Feature Alignment: Concept Activation-Guided Contrastive Learning—0
HyCIR: Boosting Zero-Shot Composed Image Retrieval with Synthetic Labels—0
Bootstrapping Vision-language Models for Self-supervised Remote Physiological Measurement—0
Hyperbolic Contrastive Learning for Hierarchical 3D Point Cloud Embedding—0
An Image-based Approach of Task-driven Driving Scene Categorization—0
Khan-GCL: Kolmogorov-Arnold Network Based Graph Contrastive Learning with Hard Negatives—0
Label-free Prediction of Vascular Connectivity in Perfused Microvascular Networks in vitro—0
Generalization Analysis for Deep Contrastive Representation Learning—0
Generalization Analysis for Contrastive Representation Learning under Non-IID Settings—0
Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System—0
Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection—0
Contrastive Learning Meets Transfer Learning: A Case Study In Medical Image Analysis—0
Generalization Analysis for Contrastive Representation Learning—0
HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views—0
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning—0
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series—0
Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning—0
HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis—0
TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance—0
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation—0
K-Diag: Knowledge-enhanced Disease Diagnosis in Radiographic Imaging—0
CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy—0
Contrastive Learning is Just Meta-Learning—0
HyperTaxel: Hyper-Resolution for Taxel-Based Tactile Signals Through Contrastive Learning—0
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering—0
HySurvPred: Multimodal Hyperbolic Embedding with Angle-Aware Hierarchical Contrastive Learning and Uncertainty Constraints for Survival Prediction—0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
I^2MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation—0
GenCAD-Self-Repairing: Feasibility Enhancement for 3D CAD Generation—0
Contrastive Learning in Memristor-based Neuromorphic Systems—0
GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors—0
GEmo-CLAP: Gender-Attribute-Enhanced Contrastive Language-Audio Pretraining for Accurate Speech Emotion Recognition—0
Interactive Contrastive Learning for Self-supervised Entity Alignment—0
i-Code: An Integrative and Composable Multimodal Learning Framework—0
I-Con: A Unifying Framework for Representation Learning—0
ICPC: Instance-Conditioned Prompting with Contrastive Learning for Semantic Segmentation—0
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling—0
Controllable Augmentations for Video Representation Learning—0
Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Images—0
Keyword-Based Diverse Image Retrieval by Semantics-aware Contrastive Learning and Transformer—0
Identical and Fraternal Twins: Fine-Grained Semantic Contrastive Learning of Sentence Representations—0
GCL: Gradient-Guided Contrastive Learning for Medical Image Segmentation with Multi-Perspective Meta Labels—0
Identification of morphological fingerprint in perinatal brains using quasi-conformal mapping and contrastive learning—0
Progressive Domain Adaptation with Contrastive Learning for Object Detection in the Satellite Imagery—0
ControlRec: Bridging the Semantic Gap between Language Model and Personalized Recommendation—0
Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models—0
GCC: Generative Calibration Clustering—0
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients—0
Id-Free Person Similarity Learning—0
A dual contrastive framework—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