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 2176–2200 of 6661 papers

TitleStatusHype
A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches—0
Denoising Multi-modal Sequential Recommenders with Contrastive Learning—0
CLIP-Lung: Textual Knowledge-Guided Lung Nodule Malignancy Prediction—0
Denoising Long- and Short-term Interests for Sequential Recommendation—0
A Hybrid Egocentric Activity Anticipation Framework via Memory-Augmented Recurrent and One-Shot Representation Forecasting—0
A Cross Branch Fusion-Based Contrastive Learning Framework for Point Cloud Self-supervised Learning—0
A Tale of Two Classes: Adapting Supervised Contrastive Learning to Binary Imbalanced Datasets—0
A Hybrid CNN-Transformer Architecture with Frequency Domain Contrastive Learning for Image Deraining—0
Del Visual al Auditivo: Sonorización de Escenas Guiada por Imagen—0
CLIP-Hand3D: Exploiting 3D Hand Pose Estimation via Context-Aware Prompting—0
AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction—0
MMPKUBase: A Comprehensive and High-quality Chinese Multi-modal Knowledge Graph—0
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing—0
Delving into E-Commerce Product Retrieval with Vision-Language Pre-training—0
3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations—0
DEHRFormer: Real-time Transformer for Depth Estimation and Haze Removal from Varicolored Haze Scenes—0
CLIP-FLow: Contrastive Learning by semi-supervised Iterative Pseudo labeling for Optical Flow Estimation—0
From Good to Best: Two-Stage Training for Cross-lingual Machine Reading Comprehension—0
Defending Multimodal Backdoored Models by Repulsive Visual Prompt Tuning—0
CLIP-Driven Open-Vocabulary 3D Scene Graph Generation via Cross-Modality Contrastive Learning—0
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses—0
CLIPC8: Face liveness detection algorithm based on image-text pairs and contrastive learning—0
A Hybrid Approach for Document Layout Analysis in Document images—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
Deep Temporal Contrastive Clustering—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