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 1276–1300 of 6661 papers

TitleStatusHype
Contrastive Federated Learning with Tabular Data Silos—0
EyeCLIP: A visual-language foundation model for multi-modal ophthalmic image analysisCode2
ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors—0
Enhancing Graph Contrastive Learning with Reliable and Informative Augmentation for RecommendationCode0
CustomContrast: A Multilevel Contrastive Perspective For Subject-Driven Text-to-Image Customization—0
Towards Building a Robust Knowledge Intensive Question Answering Model with Large Language Models—0
GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priors—0
GS-PT: Exploiting 3D Gaussian Splatting for Comprehensive Point Cloud Understanding via Self-supervised Learning—0
Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition—0
VidLPRO: A Video-Language Pre-training Framework for Robotic and Laparoscopic Surgery—0
Dual-stream Feature Augmentation for Domain GeneralizationCode1
Fine-Grained Representation Learning via Multi-Level Contrastive Learning without Class PriorsCode0
Contrastive Learning-based User Identification with Limited Data on Smart Textiles—0
Dual-Level Cross-Modal Contrastive ClusteringCode0
Self-Supervised Contrastive Learning for Videos using Differentiable Local AlignmentCode0
Few-Shot Continual Learning for Activity Recognition in Classroom Surveillance Images—0
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain RecommendationCode0
MOSMOS: Multi-organ segmentation facilitated by medical report supervision—0
SITAR: Semi-supervised Image Transformer for Action Recognition—0
Dual Advancement of Representation Learning and Clustering for Sparse and Noisy ImagesCode0
Towards Generative Class Prompt Learning for Fine-grained Visual RecognitionCode0
EEG-Language Modeling for Pathology Detection—0
MOOSS: Mask-Enhanced Temporal Contrastive Learning for Smooth State Evolution in Visual Reinforcement LearningCode0
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning—0
Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual LearningCode1
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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