On Depth and Complexity of Generative Adversarial Networks
Although generative adversarial networks (GANs) have achieved state-of-the-art results in generating realistic look- ing images, they are often parameterized by neural net- works with relatively few learnable weights compared to those that are used for discriminative tasks. We argue that this is sub...
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| Format: | Others |
| Language: | English |
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KTH, Skolan för datavetenskap och kommunikation (CSC)
2017
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217293 |