Abstract
Current solutions to discriminative and generative tasks in computer vision exist separately and often lack interpretability and explainability. Using faces as our application domain, here we present an architecture that is based around two core ideas that address these issues: first, our framework learns an unsupervised, low-dimensional embedding of faces using an adversarial autoencoder that is able to synthesize high-quality face images. Second, a supervised disentanglement splits the low-dimensional embedding vector into four sub-vectors, each of which contains separated information about one of four major face attributes (pose, identity, expression, and style) that can be used both for discriminative tasks and for manipulating all four attributes in an explicit manner. The resulting architecture achieves state-of-the-art image quality, good discrimination and face retrieval results on each of the four attributes, and supports various face editing tasks using a face representation of only 99 dimensions. Finally, we apply the architecture's robust image synthesis capabilities to visually debug label-quality issues in an existing face dataset.
Original language | English |
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Title of host publication | Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 579-588 |
Number of pages | 10 |
ISBN (Electronic) | 9781728150239 |
DOIs | |
Publication status | Published - 2019 Oct |
Event | 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 - Seoul, Korea, Republic of Duration: 2019 Oct 27 → 2019 Oct 28 |
Publication series
Name | Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019 |
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Conference
Conference | 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 |
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Country/Territory | Korea, Republic of |
City | Seoul |
Period | 19/10/27 → 19/10/28 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- Adversarial autoencoder
- Disentanglement
- Face identification
- Facial expression recognition
- Image generation
ASJC Scopus subject areas
- Computer Science Applications
- Computer Vision and Pattern Recognition