Bias Remediation in Driver Drowsiness Detection Systems Using Generative Adversarial Networks
نویسندگان
چکیده
منابع مشابه
Automatic Colorization of Grayscale Images Using Generative Adversarial Networks
Automatic colorization of gray scale images poses a unique challenge in Information Retrieval. The goal of this field is to colorize images which have lost some color channels (such as the RGB channels or the AB channels in the LAB color space) while only having the brightness channel available, which is usually the case in a vast array of old photos and portraits. Having the ability to coloriz...
متن کاملDriver Drowsiness Detection System Using Image Processing
Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy a state which they often fail to recognize early enough according to the experts. Studies show that around one quarter of all serious motorway accidents are attributable to sleepy drivers in need of a rest, meaning that drowsiness causes more road accidents than drink-driving. Attention assist ...
متن کاملDriver Drowsiness Detection Using Multi-feature Analysis
now a day’s Road accidents are common in developed as well as developing countries. These accidents happen due to different different reasons like sleeping disorders, working in night shift or more than eight hours as over time, side effects of medicine, alcohol, speeding, freakishness of teenager’s etc. One of the most important reasons is drowsiness. Drowsiness means sleepiness, which affects...
متن کاملSpectral Image Visualization Using Generative Adversarial Networks
Spectral images captured by satellites and radiotelescopes are analyzed to obtain information about geological compositions distributions, distant asters as well as undersea terrain. Spectral images usually contain tens to hundreds of continuous narrow spectral bands and are widely used in various fields. But the vast majority of those image signals are beyond the visible range, which calls for...
متن کاملCreating Virtual Universes Using Generative Adversarial Networks
Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology. This often relies critically on high fidelity numerical simulations, which are prohibitively computationally expensive. The application of deep learning techniques to generative modeling is renewing interest in using high dimensional density estimators as computationally inexpensive emu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2981912