Generative Adversarial Networks for Image Generation
Home > Computer & Internet > Computing: general > Health & safety aspects of computing > Generative Adversarial Networks for Image Generation
Generative Adversarial Networks for Image Generation

Generative Adversarial Networks for Image Generation


     0     
5
4
3
2
1



Out of Stock


Notify me when this book is in stock
About the Book

Generative adversarial networks (GANs) were introduced by Ian Goodfellow and his co-authors including Yoshua Bengio in 2014, and were to referred by Yann Lecun (Facebook's AI research director) as "the most interesting idea in the last 10 years in ML." GANs' potential is huge, because they can learn to mimic any distribution of data, which means they can be taught to create worlds similar to our own in any domain: images, music, speech, prose. They are robot artists in a sense, and their output is remarkable - poignant even. In 2018, Christie's sold a portrait that had been generated by a GAN for $432,000.

Although image generation has been challenging, GAN image generation has proved to be very successful and impressive. However, there are two remaining challenges for GAN image generation: the quality of the generated image and the training stability. This book first provides an overview of GANs, and then discusses the task of image generation and the details of GAN image generation. It also investigates a number of approaches to address the two remaining challenges for GAN image generation. Additionally, it explores three promising applications of GANs, including image-to-image translation, unsupervised domain adaptation and GANs for security. This book appeals to students and researchers who are interested in GANs, image generation and general machine learning and computer vision.


About the Author:

Xudong Mao is currently a Postdoctoral Fellow at the Hong Kong Polytechnic University. His research interests are in the areas of computer vision and deep learning, especially generative adversarial networks and unsupervised learning. His research work has been published in top-ranked journals and conferences in the area, such as TPAMI, ICCV, and IJCAI. Dr. Mao's paper 'Least squares generative adversarial networks' has, to date (November 2020), been cited more than 1700 times since it was published in 2017 at the ICCV conference.

Qing Li is currently a Chair Professor at the Hong Kong Polytechnic University. He also serves/served as a Guest Professor of Zhejiang University, an Adjunct Professor of the University of Science and Technology of China, and a Visiting Professor at the Wuhan University and the Hunan University. His research interests include database modeling, multimedia retrieval and management, social media computing and e-learning systems. Dr. Li has published over 400 papers in technical journals and international conferences in these areas, and is actively involved in the research community by serving as a journal reviewer, program committee chair/co-chair, and as an organizer/co-organizer of numerous international conferences. Currently he is the Chairman of the Hong Kong Web Society, a councillor of the Database Society of Chinese Computer Federation (CCF), a member of the CCF Big Data Experts Committee, and a member of the international WISE Society's steering committee.


Best Sellers



Product Details
  • ISBN-13: 9789813360471
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Height: 239 mm
  • No of Pages: 77
  • Spine Width: 10 mm
  • Width: 198 mm
  • ISBN-10: 981336047X
  • Publisher Date: 27 Mar 2021
  • Binding: Hardback
  • Language: English
  • Returnable: Y
  • Weight: 367 gr


Similar Products

Add Photo
Add Photo

Customer Reviews

REVIEWS      0     
Click Here To Be The First to Review this Product
Generative Adversarial Networks for Image Generation
Springer -
Generative Adversarial Networks for Image Generation
Writing guidlines
We want to publish your review, so please:
  • keep your review on the product. Review's that defame author's character will be rejected.
  • Keep your review focused on the product.
  • Avoid writing about customer service. contact us instead if you have issue requiring immediate attention.
  • Refrain from mentioning competitors or the specific price you paid for the product.
  • Do not include any personally identifiable information, such as full names.

Generative Adversarial Networks for Image Generation

Required fields are marked with *

Review Title*
Review
    Add Photo Add up to 6 photos
    Would you recommend this product to a friend?
    Tag this Book Read more
    Does your review contain spoilers?
    What type of reader best describes you?
    I agree to the terms & conditions
    You may receive emails regarding this submission. Any emails will include the ability to opt-out of future communications.

    CUSTOMER RATINGS AND REVIEWS AND QUESTIONS AND ANSWERS TERMS OF USE

    These Terms of Use govern your conduct associated with the Customer Ratings and Reviews and/or Questions and Answers service offered by Bookswagon (the "CRR Service").


    By submitting any content to Bookswagon, you guarantee that:
    • You are the sole author and owner of the intellectual property rights in the content;
    • All "moral rights" that you may have in such content have been voluntarily waived by you;
    • All content that you post is accurate;
    • You are at least 13 years old;
    • Use of the content you supply does not violate these Terms of Use and will not cause injury to any person or entity.
    You further agree that you may not submit any content:
    • That is known by you to be false, inaccurate or misleading;
    • That infringes any third party's copyright, patent, trademark, trade secret or other proprietary rights or rights of publicity or privacy;
    • That violates any law, statute, ordinance or regulation (including, but not limited to, those governing, consumer protection, unfair competition, anti-discrimination or false advertising);
    • That is, or may reasonably be considered to be, defamatory, libelous, hateful, racially or religiously biased or offensive, unlawfully threatening or unlawfully harassing to any individual, partnership or corporation;
    • For which you were compensated or granted any consideration by any unapproved third party;
    • That includes any information that references other websites, addresses, email addresses, contact information or phone numbers;
    • That contains any computer viruses, worms or other potentially damaging computer programs or files.
    You agree to indemnify and hold Bookswagon (and its officers, directors, agents, subsidiaries, joint ventures, employees and third-party service providers, including but not limited to Bazaarvoice, Inc.), harmless from all claims, demands, and damages (actual and consequential) of every kind and nature, known and unknown including reasonable attorneys' fees, arising out of a breach of your representations and warranties set forth above, or your violation of any law or the rights of a third party.


    For any content that you submit, you grant Bookswagon a perpetual, irrevocable, royalty-free, transferable right and license to use, copy, modify, delete in its entirety, adapt, publish, translate, create derivative works from and/or sell, transfer, and/or distribute such content and/or incorporate such content into any form, medium or technology throughout the world without compensation to you. Additionally,  Bookswagon may transfer or share any personal information that you submit with its third-party service providers, including but not limited to Bazaarvoice, Inc. in accordance with  Privacy Policy


    All content that you submit may be used at Bookswagon's sole discretion. Bookswagon reserves the right to change, condense, withhold publication, remove or delete any content on Bookswagon's website that Bookswagon deems, in its sole discretion, to violate the content guidelines or any other provision of these Terms of Use.  Bookswagon does not guarantee that you will have any recourse through Bookswagon to edit or delete any content you have submitted. Ratings and written comments are generally posted within two to four business days. However, Bookswagon reserves the right to remove or to refuse to post any submission to the extent authorized by law. You acknowledge that you, not Bookswagon, are responsible for the contents of your submission. None of the content that you submit shall be subject to any obligation of confidence on the part of Bookswagon, its agents, subsidiaries, affiliates, partners or third party service providers (including but not limited to Bazaarvoice, Inc.)and their respective directors, officers and employees.

    Accept

    New Arrivals



    Inspired by your browsing history


    Your review has been submitted!

    You've already reviewed this product!