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 3001–3025 of 6661 papers

TitleStatusHype
Generalization Analysis for Deep Contrastive Representation Learning—0
HyCIR: Boosting Zero-Shot Composed Image Retrieval with Synthetic Labels—0
Generalization Analysis for Contrastive Representation Learning under Non-IID Settings—0
Hyperbolic Contrastive Learning for Hierarchical 3D Point Cloud Embedding—0
Contrastive Learning Meets Transfer Learning: A Case Study In Medical Image Analysis—0
Generalization Analysis for Contrastive Representation Learning—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
Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System—0
Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection—0
Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning—0
Joint Generative-Contrastive Representation Learning for Anomalous Sound Detection—0
HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views—0
Joint Learning of Context and Feedback Embeddings in Spoken Dialogue—0
CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy—0
Contrastive Learning is Just Meta-Learning—0
HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis—0
GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing—0
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation—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
An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling—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