Improving One-Shot NAS by Suppressing the Posterior Fading

Li, Xiang; Chen, Lin; Li, Chuming; Sun, Ming; Wu, Wei; Yan, Junjie; Ouyang, Wanli

doi:10.1109/cvpr42600.2020.01385

Cited by 74 publications

(36 citation statements)

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“…Although channel attention has shown the highest model stability in our experiments, other attention modules could also be utilized to fulfill additional tasks. Powerful spatial attention modules such as the non-local layer or graph reasoning layer could be optimized and then applied to our system for the accurate classification of myocardial scar in different coronary artery territories ( 32 , 33 ). Since the image-shifting is a commonly encountered challenge, newer outlier detection algorithms could be utilized to shift the generated images more effectively.…”

Section: Discussionmentioning

confidence: 99%

Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy

Sharma

Zhao

Mentkowski

et al. 2021

Front. Cardiovasc. Med.

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Contrast-enhanced cardiac magnetic resonance imaging (MRI) is routinely used to determine myocardial scar burden and make therapeutic decisions for coronary revascularization. Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. We report a modified Generative Adversarial Network (GAN) augmentation method to improve the binary classification of myocardial scar using both pre-clinical and clinical approaches. For the initial training of the MobileNetV2 platform, we used the images generated from a high-field (9.4T) cardiac MRI of a mouse model of acute myocardial infarction (MI). Once the system showed 100% accuracy for the classification of acute MI in mice, we tested the translational significance of this approach in 91 patients with an ischemic myocardial scar, and 31 control subjects without evidence of myocardial scarring. To obtain a comparable augmentation dataset, we rotated scar images 8-times and control images 72-times, generating a total of 6,684 scar images and 7,451 control images. In humans, the use of Progressive Growing GAN (PGGAN)-based augmentation showed 93% classification accuracy, which is far superior to conventional automated modules. The use of other attention modules in our CNN further improved the classification accuracy by up to 5%. These data are of high translational significance and warrant larger multicenter studies in the future to validate the clinical implications.

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Section: Discussionmentioning

confidence: 99%

Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy

Sharma

Zhao

Mentkowski

et al. 2021

Front. Cardiovasc. Med.

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“…Recently, Liu et al [34] propose a DARTs model that replaces the discrete searching process with a continuously differentiable strategy, allowing gradient descent-based architecture optimization and resulting in exponentially faster searching speed. However, such continuously differentiable strategy is less likely to explore an optimal architecture [35], [36], [37], [38] compared with RLbased methods. Fig.…”

Section: B Neural Architecture Searchmentioning

confidence: 99%

Neural Architecture Searching for Facial Attributes-based Depression Recognition

Chen¹,

Xiao²,

Zhang³

et al. 2022

Preprint

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Recent studies show that depression can be partially reflected from human facial attributes. Since facial attributes have various data structure and carry different information, existing approaches fail to specifically consider the optimal way to extract depression-related features from each of them, as well as investigates the best fusion strategy. In this paper, we propose to extend Neural Architecture Search (NAS) technique for designing an optimal model for multiple facial attributes-based depression recognition, which can be efficiently and robustly implemented in a small dataset. Our approach first conducts a warmer up step to the feature extractor of each facial attribute, aiming to largely reduce the search space and providing customized architecture, where each feature extractor can be either a Convolution Neural Networks (CNN) or Graph Neural Networks (GNN). Then, we conduct an end-to-end architecture search for all feature extractors and the fusion network, allowing the complementary depression cues to be optimally combined with less redundancy. The experimental results on AVEC 2016 dataset show that the model explored by our approach achieves breakthrough performance with 27% and 30% RMSE and MAE improvements over the existing state-of-the-art. In light of these findings, this paper provides solid evidences and a strong baseline for applying NAS to time-series data-based mental health analysis.

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“…Because inefficient search strategies require a large number of GPUs, many NAS methods cannot be implemented given limited computational resources. To address these challenges, much recent work dedicates to developing effective methods which can reduce the computational costs of performance evaluation, e.g., surrogate-assisted evolutionary algorithms (SAEAs) [33,42,43], information reuse [44,45], one-shot neural architecture search [46][47][48][49][50], among many others.…”

Section: Neural Architecture Searchmentioning

confidence: 99%

From federated learning to federated neural architecture search: a survey

Zhu

Zhang

Jin

2021

Complex Intell. Syst.

129

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Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern. Meanwhile, neural architecture search has become very popular in deep learning for automatically tuning the architecture and hyperparameters of deep neural networks. While both federated learning and neural architecture search are faced with many open challenges, searching for optimized neural architectures in the federated learning framework is particularly demanding. This survey paper starts with a brief introduction to federated learning, including both horizontal, vertical, and hybrid federated learning. Then neural architecture search approaches based on reinforcement learning, evolutionary algorithms and gradient-based are presented. This is followed by a description of federated neural architecture search that has recently been proposed, which is categorized into online and offline implementations, and single- and multi-objective search approaches. Finally, remaining open research questions are outlined and promising research topics are suggested.

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Improving One-Shot NAS by Suppressing the Posterior Fading

Cited by 74 publications

References 1 publication

Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy

Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy

Neural Architecture Searching for Facial Attributes-based Depression Recognition

From federated learning to federated neural architecture search: a survey

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