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Paper Abstract and Keywords
Presentation 2026-07-24 10:00
Local Attribute Control and Bias Evaluation of Image Generative Models Using Concept Bottleneck
Keiichiro Oishi, Haruya Ishizaki, Hiroyuki Nakagawa (Okayama Univ.) ICM2026-11 SC2026-17
Abstract (in Japanese) (See Japanese page) 
(in English) While high-precision image generation models, including Generative Adversarial Networks, have become widespread, their internal representations remain black boxes. This presents challenges in achieving precise, human-intended control over local attributes and in mitigating biases present in the output. In this study, we apply a concept bottleneck model with human-interpretable intermediate layers to verify the effectiveness of local attribute control within the latent space of image generation models. We also investigate approaches to mitigate unintended biases that arise as a limitation of such control. Our evaluation focuses on four key aspects: the choice of attribute predictors, intervention intensity, bias, and generation fidelity. Regarding bias specifically, we visualize the unintended entanglement of attributes in the latent space that occurs during manipulation and discuss the effectiveness of an approach that cancels out unwanted bias components through mathematical vector operations. Through this evaluation, we demonstrate the potential of latent space control in achieving a high degree of controllability in image generation models.
Keyword (in Japanese) (See Japanese page) 
(in English) Image generation model / Concept Bottleneck Models / CB-AE / StyleGAN / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 129, SC2026-17, pp. 48-55, July 2026.
Paper # SC2026-17 
Date of Issue 2026-07-16 (ICM, SC) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF ICM2026-11 SC2026-17

Conference Information
Committee SC ICM  
Conference Date 2026-07-23 - 2026-07-24 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SC 
Conference Code 2026-07-SC-ICM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Local Attribute Control and Bias Evaluation of Image Generative Models Using Concept Bottleneck 
Sub Title (in English)  
Keyword(1) Image generation model  
Keyword(2) Concept Bottleneck Models  
Keyword(3) CB-AE  
Keyword(4) StyleGAN  
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1st Author's Name Keiichiro Oishi  
1st Author's Affiliation Okayama University (Okayama Univ.)
2nd Author's Name Haruya Ishizaki  
2nd Author's Affiliation Okayama University (Okayama Univ.)
3rd Author's Name Hiroyuki Nakagawa  
3rd Author's Affiliation Okayama University (Okayama Univ.)
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Speaker Author-1 
Date Time 2026-07-24 10:00:00 
Presentation Time 25 minutes 
Registration for SC 
Paper # ICM2026-11, SC2026-17 
Volume (vol) vol.126 
Number (no) no.128(ICM), no.129(SC) 
Page pp.48-55 
#Pages 8 
Date of Issue 2026-07-16 (ICM, SC) 

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