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Generative adversarial network: Difference between revisions
Generative adversarial network (view source)
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** Colorize with GAN. Only transmit luminance (Y of YUV) | ** Colorize with GAN. Only transmit luminance (Y of YUV) | ||
** The paper claims 72% BDBR reduction compared to HM 16.0. | ** The paper claims 72% BDBR reduction compared to HM 16.0. | ||
==Important Papers== | |||
* [https://arxiv.org/abs/1811.10597 GAN Dissection: Visualizing and Understanding Generative Adversarial Networks] | |||
** Basically, each "unit" or channel of the intermediate representations correspond to some features like windows or trees | |||
** Dissection: Identifying which units correspond to features can be done by visualizing each channel as a heatmap. Then threshold the heatmap so each value is binary 0/1. Calculate the IOU between the heatmap and the segmented feature in the generated picture. | |||
** Intervention: By zeroing out channels, you can remove windows or trees from the generated image. Alternatively you can add windows or trees at specific locations by activating the neurons at that location. | |||
** This is fairly specific to CNN architectures where there is a locality correspondence between the intermediate representations and the output image. | |||
==Resources== | ==Resources== | ||
* [https://github.com/soumith/ganhacks Tricks for Training GANs] | * [https://github.com/soumith/ganhacks Tricks for Training GANs] | ||