Face Attribute Modification Using Fine-Tuned Attribute-Modification Network
- Authors
- Islam, Naeem Ul; Park, Jaebyung
- Issue Date
- May-2020
- Publisher
- MDPI
- Keywords
- generative adversarial network; convolutional neural network; fine-tuned attribute-modification network; autoencoders
- Citation
- ELECTRONICS, v.9, no.5
- Journal Title
- ELECTRONICS
- Volume
- 9
- Number
- 5
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/80765
- DOI
- 10.3390/electronics9050743
- ISSN
- 2079-9292
- Abstract
- Multi-domain image-to-image translation with the desired attributes is an important approach for modifying single or multiple attributes of a face image, but is still a challenging task in the computer vision field. Previous methods were based on either attribute-independent or attribute-dependent approaches. The attribute-independent approach, in which the modification is performed in the latent representation, has performance limitations because it requires paired data for changing the desired attributes. In contrast, the attribute-dependent approach is effective because it can modify the required features while maintaining the information in the given image. However, the attribute-dependent approach is sensitive to attribute modifications performed while preserving the face identity, and requires a careful model design for generating high-quality results. To address this problem, we propose a fine-tuned attribute modification network (FTAMN). The FTAMN comprises a single generator and two discriminators. The discriminators use the modified image in two configurations with the binary attributes to fine tune the generator such that the generator can generate high-quality attribute-modification results. Experimental results obtained using the CelebA dataset verify the feasibility and effectiveness of the proposed FTAMN for editing multiple facial attributes while preserving the other details.
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Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
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