Synthesizing Programs for Images usingReinforced Adversarial Learning: Difference between revisions
Jump to navigation
Jump to search
(Created page with "'''Synthesizing Programs for Images usingReinforced Adversarial Learning: ''' Summary of the ICML 2018 paper http://proceedings.mlr.press/v80/ganin18a.html") |
No edit summary |
||
| Line 1: | Line 1: | ||
'''Synthesizing Programs for Images usingReinforced Adversarial Learning: ''' Summary of the ICML 2018 paper http://proceedings.mlr.press/v80/ganin18a.html | '''Synthesizing Programs for Images usingReinforced Adversarial Learning: ''' Summary of the ICML 2018 paper http://proceedings.mlr.press/v80/ganin18a.html | ||
= Presented by = | |||
1. Nekoei, Hadi [Quest ID: 20727088] | |||
= Motivation = | |||
Conventional neural generative models have major problems. | |||
* Firstly, it is not clear how to inject knowledge to the model about the data. | |||
* Secondly, latent space is not easily interpretable. | |||
The provided solution in this paper is to generate programs to incorporate tools, e.g. graphics editors, illustration software, CAD. and creating more meaningful API(sequence of complex actions vs raw pixels). | |||
Revision as of 13:45, 23 October 2018
Synthesizing Programs for Images usingReinforced Adversarial Learning: Summary of the ICML 2018 paper http://proceedings.mlr.press/v80/ganin18a.html
Presented by
1. Nekoei, Hadi [Quest ID: 20727088]
Motivation
Conventional neural generative models have major problems.
- Firstly, it is not clear how to inject knowledge to the model about the data.
- Secondly, latent space is not easily interpretable.
The provided solution in this paper is to generate programs to incorporate tools, e.g. graphics editors, illustration software, CAD. and creating more meaningful API(sequence of complex actions vs raw pixels).