Accurate scene text spotting models ask for effective data augmentation algorithms. This paper presents a novel data augmentation algorithm called AdvMix that integrates techniques of adversarial attack and mixup. First, it utilizes the PGD method to synthesize adversarial samples. Second, it is the first work to implement feature-wise mixup between original data and the associated adversarial sample to enhance data augmentation. AdvMix has been evaluated over ICDAR2013 and ICDAR2015 datasets, showing its superior performance in improving accuracy of scene text spotting models.
Respiratory diseases are leading causes of death worldwide, and failure to detect diseases at an early stage can threaten people’s lives. Previous research has pointed out that deep learning and machine learning are valid alternative strategies to detect respiratory diseases without the presence of a doctor. Thus, it is worthwhile to develop an automatic respiratory disease detection system. This paper proposes a deep neural network with a blocking variable, namely Blnet, to classify respiratory sound, which integrates the strength of the ResNet, GoogleNet, and the self-attention mechanism. To solve the non-IID data problem, a two-stage training process with the blocking variable was developed. In addition, the mix-up data augmentation within the clusters was used to address the imbalanced data problem. The performance of the Blnet was tested on the ICBHI 2017 data, and the model achieved 79.13% specificity and 66.31% sensitivity, with an average score of 72.72%, which is a 4.22% improvement in the average score and a 12.61% improvement in sensitivity over the state-of-the-art results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.