Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation

Wang, Zhengwei; Healy, Graham; Smeaton, Alan F.; Ward, Tomás E.

doi:10.1007/s12559-019-09670-y

Cited by 37 publications

(26 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generated images by a GAN are shown to the participants and the corresponding recorded neural responses are used to evaluate the GAN performance. Figurecompiledfrom[49].…”

mentioning

confidence: 99%

Pros and cons of GAN evaluation measures

Borji¹

2019

Computer Vision and Image Understanding

655

438

View full text Add to dashboard Cite

Generative models, in particular generative adversarial networks (GANs), have gained significant attention in recent years. A number of GAN variants have been proposed and have been utilized in many applications. Despite large strides in terms of theoretical progress, evaluating and comparing GANs remains a daunting task. While several measures have been introduced, as of yet, there is no consensus as to which measure best captures strengths and limitations of models and should be used for fair model comparison. As in other areas of computer vision and machine learning, it is critical to settle on one or few good measures to steer the progress in this field. In this paper, I review and critically discuss more than 24 quantitative and 5 qualitative measures for evaluating generative models with a particular emphasis on GAN-derived models. I also provide a set of 7 desiderata followed by an evaluation of whether a given measure or a family of measures is compatible with them.

show abstract

“…Generated images by a GAN are shown to the participants and the corresponding recorded neural responses are used to evaluate the GAN performance. Figurecompiledfrom[49].…”

mentioning

confidence: 99%

Pros and cons of GAN evaluation measures

Borji¹

2019

Computer Vision and Image Understanding

655

438

View full text Add to dashboard Cite

show abstract

“…These generator and discriminator models are trained together in a zero-sum game (i.e. in an adversarial fashion) such that the examples generated by the generator model maximize the loss of the discriminator model [ 82 , 83 ].…”

Section: Overview Of Deep Learningmentioning

confidence: 99%

Deep Learning in Mining Biological Data

et al. 2021

View full text Add to dashboard Cite

Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems. To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

show abstract

“…In this setting, complex feedback, through open or closed loops, can give further insights into the functional mechanics of cortical activity in the human brain, and has much experimental and therapeutic potential [ 176 , 177 ]. Capturing higher quality TMS-EEG data, with a reduction in noise, has become a slightly less arduous task, and has allowed researchers to develop novel techniques to identify and understand patterns of clinical significance [ 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 ]. We believe our survey highlights challenges and proposes solutions related to TMS-EEG experiments.…”

Section: Existing Challenges and Future Goalsmentioning

confidence: 99%

Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures

Varone

Hussain

Sheikh

et al. 2021

Sensors

View full text Add to dashboard Cite

Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulation as they can be contaminated by artifacts. Over the last two decades, significant developments in EEG amplifiers, TMS-compatible technology, customized hardware and open source software have enabled researchers to develop approaches which can substantially reduce TMS-induced artifacts. In TMS-EEG experiments, various physiological and external occurrences have been identified and attempts have been made to minimize or remove them using online techniques. Despite these advances, technological issues and methodological constraints prevent straightforward recordings of early TEPs components. To the best of our knowledge, there is no review on both TMS-EEG artifacts and EEG technologies in the literature to-date. Our survey aims to provide an overview of research studies in this field over the last 40 years. We review TMS-EEG artifacts, their sources and their waveforms and present the state-of-the-art in EEG technologies and front-end characteristics. We also propose a synchronization toolbox for TMS-EEG laboratories. We then review subject preparation frameworks and online artifacts reduction maneuvers for improving data acquisition and conclude by outlining open challenges and future research directions in the field.

show abstract

Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation

Cited by 37 publications

References 30 publications

Pros and cons of GAN evaluation measures

Pros and cons of GAN evaluation measures

Deep Learning in Mining Biological Data

Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures

Contact Info

Product

Resources

About