Projects per year
Abstract
Variational Autoencoders (VAE) learn a latent representation of image data that allows natural image generation and manipulation. However, they struggle to generate sharp images.To address this problem, we propose a hierarchy of VAEs analogous to a Laplacian pyramid. Each network models a single pyramid level, and is conditioned on the coarser levels. The Laplacian architecture allows for novel image editing applications that take advantage of the coarse to fine structure of the model. Our method achieves lower reconstruction error in terms of MSE, which is the loss function of the VAE and is not directly minimised in our model. Furthermore, the reconstructions generated by the proposed model are preferred over those from the VAE by human evaluators.
Original language | English |
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Number of pages | 9 |
DOIs | |
Publication status | Published - 11 Dec 2017 |
Event | CVMP 2017: The European Conference on Visual Media Production - Duration: 11 Dec 2017 → 12 Dec 2017 |
Conference
Conference | CVMP 2017: The European Conference on Visual Media Production |
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Period | 11/12/17 → 12/12/17 |
Keywords
- Deep Neural Networks
- Generative Models
- Faces
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
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Dive into the research topics of 'Laplacian Pyramid of Conditional Variational Autoencoders'. Together they form a unique fingerprint.Projects
- 2 Finished
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Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)
Cosker, D. (PI), Bilzon, J. (CoI), Campbell, N. (CoI), Cazzola, D. (CoI), Colyer, S. (CoI), Fincham Haines, T. (CoI), Hall, P. (CoI), Kim, K. I. (CoI), Lutteroth, C. (CoI), McGuigan, P. (CoI), O'Neill, E. (CoI), Richardt, C. (CoI), Salo, A. (CoI), Seminati, E. (CoI), Tabor, A. (CoI) & Yang, Y. (CoI)
Engineering and Physical Sciences Research Council
1/09/15 → 28/02/21
Project: Research council
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EPSRC Centre for Doctoral Training in Digital Entertainment
Willis, P. (PI)
1/04/14 → 30/09/22
Project: Research-related funding