Batch Normalization (BN) is a common technique used to speed-up and stabilize training. On the other hand, the learnable parameters of BN are commonly used in conditional Generative Adversarial Networks (cGANs) for representing class-specific information using conditional Batch Normalization (cBN). In this paper we propose to generalize both BN and cBN using a Whitening and Coloring based batch normalization. We show that our conditional Coloring can represent categorical conditioning information which largely helps the cGAN qualitative results. Moreover, we show that full-feature whitening is important in a general GAN scenario in which the training process is known to be highly unstable. We test our approach on different datasets and using different GAN networks and training protocols, showing a consistent improvement in all the tested frameworks. Our CIFAR-10 conditioned results are higher than all previous works on this dataset.
Whitening and coloring batch transform for GANS / Siarohin, A.; Sangineto, E.; Sebe, N.. - (2019). ((Intervento presentato al convegno International Conference on Learning Representations tenutosi a New Orleans nel May 6 - May 9, 2019.
Titolo: | Whitening and coloring batch transform for GANS | |
Autori: | Siarohin, A.; Sangineto, E.; Sebe, N. | |
Autori Unitn: | ||
Titolo del volume contenente il saggio: | International Conference on Learning Representations | |
Luogo di edizione: | New Orleans | |
Casa editrice: | Open Publisher | |
Anno di pubblicazione: | 2019 | |
Codice identificativo Scopus: | 2-s2.0-85083952772 | |
Handle: | http://hdl.handle.net/11572/250678 | |
Citazione: | Whitening and coloring batch transform for GANS / Siarohin, A.; Sangineto, E.; Sebe, N.. - (2019). ((Intervento presentato al convegno International Conference on Learning Representations tenutosi a New Orleans nel May 6 - May 9, 2019. | |
Appare nelle tipologie: | 04.1 Saggio in atti di convegno (Paper in proceedings) |
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