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 3076–3100 of 6661 papers

TitleStatusHype
Multi-scale Contrastive Adaptor Learning for Segmenting Anything in Underperformed Scenes—0
Boosting Adverse Weather Crowd Counting via Multi-queue Contrastive Learning—0
Context-aware Visual Storytelling with Visual Prefix Tuning and Contrastive Learning—0
CURLing the Dream: Contrastive Representations for World Modeling in Reinforcement Learning—0
HiLight: A Hierarchy-aware Light Global Model with Hierarchical Local ConTrastive Learning—0
HateSieve: A Contrastive Learning Framework for Detecting and Segmenting Hateful Content in Multimodal Memes—0
Contrastive masked auto-encoders based self-supervised hashing for 2D image and 3D point cloud cross-modal retrieval—0
Efficient Test-Time Prompt Tuning for Vision-Language Models—0
Multimodal generative semantic communication based on latent diffusion model—0
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution—0
Dual-Channel Latent Factor Analysis Enhanced Graph Contrastive Learning for Recommendation—0
Clustering-friendly Representation Learning for Enhancing Salient Features—0
MDS-GNN: A Mutual Dual-Stream Graph Neural Network on Graphs with Incomplete Features and Structure—0
CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarningCode0
Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised LearningCode0
reCSE: Portable Reshaping Features for Sentence Embedding in Self-supervised Contrastive LearningCode0
Privacy-Preserved Taxi Demand Prediction System Utilizing Distributed Data—0
Communicate to Play: Pragmatic Reasoning for Efficient Cross-Cultural Communication in CodenamesCode0
Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models—0
Learning the Simplicity of Scattering AmplitudesCode0
Integrated Dynamic Phenological Feature for Remote Sensing Image Land Cover Change Detection—0
Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive LearningCode0
Self-Supervised Contrastive Graph Clustering Network via Structural Information Fusion—0
ComKD-CLIP: Comprehensive Knowledge Distillation for Contrastive Language-Image Pre-traning Model—0
FMiFood: Multi-modal Contrastive Learning for Food Image Classification—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