Skip to main content

Advanced Search

Projected result for your current search: 0

Search Operators:

You can use operators in your search query to narrow down your results even more. Click on the search operator to see an explanation of how it works.
Finds documents with exactly this word group, in exactly this word order and spelling (e.g. "employer branding").
Finds documents with both search terms (e.g. sales AND bonus).
Finds documents with one or the other search term (e.g. porsche OR volkswagen).
Finds documents with all search terms. Blank space is understood as AND (e.g. man robot production).
Finds documents with no appearance of the word behind NOT (e.g. ford NOT "harrison ford").
Finds documents where the search term is mentioned at least "n" times."n" may be any number (e.g. COUNT(gear)>8).
Finds documents with both search terms in any word order, permitting "n" words as a maximum distance between them. Best choose between 15 and 30 (e.g. NEAR(recruit, professionals, 20)).
Finds documents with the search term in word versions or composites. The asterisk * marks whether you wish them BEFORE, BEHIND, or BEFORE and BEHIND the search term (e.g. lightweight*, *lightweight, *lightweight*).
Finds documents with the search term in different spellings. "?" allows only one character (e.g. organi?ation).
Special characters are understood as AND (e.g. Miller Bros. & Sons).

Semantic Search

powered by (Link opens in a new window)
loading …
Log in
Top

Reprogramming GANs via Input Noise Design

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
Kangwook Lee
Changho Suh
Kannan Ramchandran
Published in
Machine Learning and Knowledge Discovery in Databases
Publisher
Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The chapter delves into the innovative approach of reprogramming GANs through input noise design, offering a method to modify the functionality of pre-trained GANs without altering their parameters. This technique is particularly notable for its ability to convert unconditional GANs into conditional GANs without requiring labeled datasets. The authors present a simple algorithm for GAN reprogramming and demonstrate its effectiveness through various controlled experiments. They also explore the applicability, feasibility, and limitations of this method, providing a comprehensive analysis of its potential and constraints. The chapter concludes by discussing unexpected behaviors of GANs and the potential of reprogramming as an alternative to traditional transfer learning methods in the context of GANs.
AI Generated

This summary of the content was generated with the help of AI.

This material is based upon work supported by the Air Force Office of Scientific Research under award number FA2386-19-1-4050.

Already a customer? Then log in here to gain access.

Customer login

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Business + Economics & Engineering + Technology"

Online-Abonnement

Springer Professional "Business + Economics & Engineering + Technology" gives you access to:

  • more than 130.000 books
  • more than 540 journals

from the following subject areas:

  • Automotive
  • Construction + Real Estate
  • Business IT + Informatics
  • Electrical Engineering + Electronics
  • Energy + Sustainability
  • Finance + Banking
  • Management + Leadership
  • Marketing + Sales
  • Mechanical Engineering + Materials
  • Surfaces + Materials Technology
  • Insurance + Risk


Secure your knowledge advantage now!

inform now

Springer Professional "Engineering + Technology"

Online-Abonnement

Springer Professional "Engineering + Technology" gives you access to:

  • more than 75.000 books
  • more than 390 journals

from the following specialised fileds:

  • Automotive
  • Business IT + Informatics
  • Construction + Real Estate
  • Electrical Engineering + Electronics
  • Energy + Sustainability
  • Mechanical Engineering + Materials
  • Surfaces + Materials Technology





 

Secure your knowledge advantage now!

inform now

Springer Professional "Business + Economics"

Online-Abonnement

Springer Professional "Business + Economics" gives you access to:

  • more than 100.000 books
  • more than 340 journals

from the following specialised fileds:

  • Construction + Real Estate
  • Business IT + Informatics
  • Finance + Banking
  • Management + Leadership
  • Marketing + Sales
  • Insurance + Risk



Secure your knowledge advantage now!

inform now

For explorers: Get to know our platforms better with a free registration.

Register for free
previous chapter Learning Implicit Generative Models by Teaching Density Estimators
next chapter On Saliency Maps and Adversarial Robustness
Title
Reprogramming GANs via Input Noise Design
Authors
Kangwook Lee
Changho Suh
Kannan Ramchandran
Copyright Year
2021
Book
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-030-67660-5

Electronic ISBN: 978-3-030-67661-2

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-67661-2_16
This content is only visible if you are logged in and have the appropriate permissions.

Premium Partner

    Image Credits
    Neuer Inhalt/© ITandMEDIA, Nagarro GmbH/© Nagarro GmbH, AvePoint Deutschland GmbH/© AvePoint Deutschland GmbH, AFB Gemeinnützige GmbH/© AFB Gemeinnützige GmbH, USU GmbH/© USU GmbH, Ferrari electronic AG/© Ferrari electronic AG