Abstract
This paper discusses an approach to the description of the structure of models capable of being trained to recognize representations of items of generative models—in particular, the architecture of a convolutional autoencoder is considered in detail. Reliable qualitative results of the operation of a convolutional encoder are also presented that show that it is valid to regard this model as generative because it is possible to implement output and sampling procedures, using as an example the solution of the problem of restoring images in missing regions.
© 2015 Optical Society of America
PDF Article
More Like This
Autoencoder-based holographic image restoration
Tomoyoshi Shimobaba, Yutaka Endo, Ryuji Hirayama, Yuki Nagahama, Takayuki Takahashi, Takashi Nishitsuji, Takashi Kakue, Atsushi Shiraki, Naoki Takada, Nobuyuki Masuda, and Tomoyoshi Ito
Appl. Opt. 56(13) F27-F30 (2017)
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription