Correlation-Concealing Adversarial Noise Injection for Improved Disentanglement in Label-Based Image Translation-Reference-Cited by-同舟云学术

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Correlation-Concealing Adversarial Noise Injection for Improved Disentanglement in Label-Based Image Translation

Published:2023 Issue: Volume:11 Page:23896-23908
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Park Seonguk¹^ORCID,Song Jookyung¹^ORCID,Han Donghoon¹^ORCID,Kwak Nojun¹^ORCID

Affiliation:

1. Department of Intelligence and Information, Seoul National University, Seoul, South Korea

Funder

National Research Foundation of Korea (NRF) through the Korean Government

Institute for Information & communication Technology Planning & evaluation (IITP) through the Korean Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10063866.pdf?arnumber=10063866

Reference24 articles.

1. Explaining and harnessing adversarial examples;goodfellow;arXiv 1412 6572,2014

2. Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

3. Sym-Parameterized Dynamic Inference for Mixed-Domain Image Translation

4. A Style-Based Generator Architecture for Generative Adversarial Networks

5. Constructing unrestricted adversarial examples with generative models;song;arXiv 1805 07894,2018

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