Bayesian contrastive Learning: An augmentation-free fault diagnosis method with limited labels and uncertainty quantification

Hassaan Ahmad; Wei Cheng; Wentao Wang; Li Liu; Zhibin Wei; Shuo Zhang; Zelin Nie; Jiangkun Yang; Xuefeng Chen

doi:10.1016/j.aei.2025.104207

Related Publications

SimCSE: Simple Contrastive Learning of Sentence Embeddings

Tianyu Gao , Xingcheng Yao , Danqi Chen

This paper presents SimCSE, a simple contrastive learning framework that greatly advances the state-of-the-art sentence embeddings. We first describe an unsupervised approach, w...

2021 Proceedings of the 2021 Conference on... 2286 citations

Unsupervised Feature Learning via Non-parametric Instance Discrimination

Zhirong Wu , Yuanjun Xiong , Stella X. Yu +1 more

Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether...

2018 3435 citations

Albumentations: Fast and Flexible Image Augmentations

Alexander Buslaev , Vladimir I. Iglovikov , Eugene Khvedchenya +3 more

Data augmentation is a commonly used technique for increasing both the size and the diversity of labeled training sets by leveraging input transformations that preserve correspo...

2020 Information 1852 citations

A survey on Image Data Augmentation for Deep Learning

Connor Shorten , Taghi M. Khoshgoftaar

Abstract Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfi...

2019 Journal Of Big Data 11041 citations

Self-Training With Noisy Student Improves ImageNet Classification

Qizhe Xie , Minh-Thang Luong , Eduard Hovy +1 more

We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet, which is 2.0% better than the state-of-the-art model that requires 3.5B weakly labeled I...

2020 2020 IEEE/CVF Conference on Computer ... 2154 citations

Publication Info

Year: 2025
Type: article
Volume: 70
Pages: 104207-104207
Citations: 0
Access: Closed

External Links

Download PDF (Free) View on DOI.org Semantic Scholar

Social Impact

Altmetric

Bayesian contrastive Learning: An augmentation-free fault diagnosis method with limited labels and uncertainty quantification

PlumX Metrics

Social media, news, blog, policy document mentions

Citation Metrics

OpenAlex

Influential

CrossRef

Cite This

APA Style

                            
                                    Hassaan Ahmad, 
                                
                                    Wei Cheng, 
                                
                                    Wentao Wang
                                
                                et al.
                            
                            (2025). 
                            Bayesian contrastive Learning: An augmentation-free fault diagnosis method with limited labels and uncertainty quantification. 
                            Advanced Engineering Informatics
                            , 70
                            
                            , 104207-104207.
                            https://doi.org/10.1016/j.aei.2025.104207

Identifiers

DOI: 10.1016/j.aei.2025.104207

Data Quality

Data completeness: 63%