Automating Generative Adversarial Networks using Neural Architecture Search: A Review

Ganepola, Vayangi Vishmi Vishara; Wirasingha, Torin

doi:10.1109/esci50559.2021.9396991

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…Moreover, given that the newly GAN-based approach of introducing a roundtrip method to the conditional GAN is proposed [43], applying it to tabular-formed data might be promising. Although the present study calculated the deep generative models using default parameters, the approach of automatically optimizing hyperparameters of the GAN-based model has been proposed [44]. The evolutionary architectural search GAN (EAS-GAN) enables the optimization of hyperparameters, including network architecture [45].…”

Section: Discussionmentioning

confidence: 99%

Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science

Watanuki,

Nomura,

Kiyota

et al. 2023

Applied Sciences

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Although a multimodal data analysis, comprising physiological and questionnaire survey data, provides better insights into addressing management science concerns, such as challenging the predictions of consumer choice behavior, studies in this field are scarce because of two obstacles: limited sample size and information privacy. This study addresses these challenges by synthesizing multimodal data using deep generative models. We obtained multimodal data by conducting an electroencephalography (EEG) experiment and a questionnaire survey on the prediction of skilled nurses. Subsequently, we validated the effectiveness of the synthesized data compared with real data regarding the similarities between these data and the predictive performance. We confirmed that the synthesized big data were almost equal to the real data using the trained models through sufficient epochs. Conclusively, we demonstrated that synthesizing data using deep generative models might overcome two significant concerns regarding multimodal data utilization, including physiological data. Our approach can contribute to the prevailing combined big data from different modalities, such as physiological and questionnaire survey data, when solving management issues.

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Section: Discussionmentioning

confidence: 99%

Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science

Watanuki,

Nomura,

Kiyota

et al. 2023

Applied Sciences

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“…GANS are difficult to include in automated frameworks as they face training challenges, like mode collapse, non-convergence, instability and vanishing gradients [42], and they come with the risk of hallucination. NAS frameworks that are specifically developed for GANs do exist (e.g., [43][44][45]), but our goal was to create a rich search space comprising different types of architectures. The two-network architectures of GANS make it very challenging to include any other types of architectures because of the significant differences in both training and architecture.…”

Section: Super-resolutionmentioning

confidence: 99%

AutoSR4EO: An AutoML Approach to Super-Resolution for Earth Observation Images

Wąsala,

Marselis,

Arp

et al. 2024

Remote Sensing

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Super-resolution (SR), a technique to increase the resolution of images, is a pre-processing step in the pipelines of applications of Earth observation (EO) data. The manual design and optimisation of SR models that are specific to every possible EO use case is a laborious process that creates a bottleneck for EO analysis. In this work, we develop an automated machine learning (AutoML) method to automate the creation of dataset-specific SR models. AutoML is the study of the automatic design of high-performance machine learning models. We present the following contributions. (i) We propose AutoSR4EO, an AutoML method for automatically constructing neural networks for SR. We design a search space based on state-of-the-art residual neural networks for SR and incorporate transfer learning. Our search space is extendable, making it possible to adapt AutoSR4EO to future developments in the field. (ii) We introduce a new real-world single-image SR (SISR) dataset, called SENT-NICFI. (iii) We evaluate the performance of AutoSR4EO on four different datasets against the performance of four state-of-the-art baselines and a vanilla AutoML SR method, with AutoSR4EO achieving the highest average ranking. Our results show that AutoSR4EO performs consistently well over all datasets, demonstrating that AutoML is a promising method for improving SR techniques for EO images.

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Neural Architecture Search for Transformers: A Survey

et al. 2022

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Transformer-based Deep Neural Network architectures have gained tremendous interest due to their effectiveness in various applications across Natural Language Processing (NLP) and Computer Vision (CV) domains. These models are the de facto choice in several language tasks, such as Sentiment Analysis and Text Summarization, replacing Long Short Term Memory (LSTM) model. Vision Transformers (ViTs) have shown better model performance than traditional Convolutional Neural Networks (CNNs) in vision applications while requiring significantly fewer parameters and training time. The design pipeline of a neural architecture for a given task and dataset is extremely challenging as it requires expertise in several interdisciplinary areas such as signal processing, image processing, optimization and allied fields. Neural Architecture Search (NAS) is a promising technique to automate the architectural design process of a Neural Network in a data-driven way using Machine Learning (ML) methods. The search method explores several architectures without requiring significant human effort, and the searched models outperform the manually built networks. In this paper, we review Neural Architecture Search techniques, targeting the Transformer model and its family of architectures such as Bidirectional Encoder Representations from Transformers (BERT) and Vision Transformers. We provide an in-depth literature review of approximately 50 state-of-theart Neural Architecture Search methods and explore future directions in this fast-evolving class of problems.

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Automating Generative Adversarial Networks using Neural Architecture Search: A Review

Cited by 8 publications

References 18 publications

Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science

Applying a Method for Augmenting Data Mixed from Two Different Sources Using Deep Generative Neural Networks to Management Science

AutoSR4EO: An AutoML Approach to Super-Resolution for Earth Observation Images

Neural Architecture Search for Transformers: A Survey

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