4 ms·

Me and the other authors of the post will be around if anyone has any questions.

te_platt 10y ago
I'm doing some research into handwriting recognition. In particular transcribing older documents. Do you see a way of applying your work here in that direction?
- colah3 10y ago
  The generative model of handwriting that we're working with here probably isn't very applicable to handwriting recognition. In principle, you could train a model like this to jointly model the text and the produced handwriting and then search for the most likely text to correspond to handwriting, but it would be a lot less efficient and likely not work as well. Instead, the natural way to apply neural networks to use a convolutional neural network. You could either predict the presence of characters at different positions and stitch them together with another program, or do an end-to-end image to sequence approach, probably using attention. If you want to visualize that kind of model, the techniques you want to use are pretty different than what we have in this article. But there are some pretty useful techniques! In particular, you could do attention visualization to understand where your model is looking as it predicts particular characters and optimization-based feature visualization to understand what different features in your model represent.
  - AstralStorm 10y ago
    You can still have CNN hallucinate most likely set of next inputs. And the kind of activity display you did later also works.
pg_is_a_butt 10y ago
why does it such so much and constantly assume i'll write another "c" character? i don't see anything worth anything.
inlineint 10y ago
How much computing power/time did it take to train the model? Had any hyperparameters optimization been done? Sorry if I missed the reference that has this information, but I think that these numbers are important in any deep learning experiment because they allow the readers to evaluate applicability of described methods to their problems.
- shancarter 10y ago
  This post has more details about the model: http://blog.otoro.net/2015/12/12/handwriting-generation-demo-in-tensorflow/ http://blog.otoro.net/2015/12/12/handwriting-generation-demo...
  - colah3 10y ago
    Note that the contribution of this article is techniques for visualizing models, not the novel itself.
jenniferx 10y ago
pretty cool i guess

I'm doing some research into handwriting recognition. In particular transcribing older documents. Do you see a way of applying your work here in that direction?

colah3 10y ago
The generative model of handwriting that we're working with here probably isn't very applicable to handwriting recognition. In principle, you could train a model like this to jointly model the text and the produced handwriting and then search for the most likely text to correspond to handwriting, but it would be a lot less efficient and likely not work as well. Instead, the natural way to apply neural networks to use a convolutional neural network. You could either predict the presence of characters at different positions and stitch them together with another program, or do an end-to-end image to sequence approach, probably using attention. If you want to visualize that kind of model, the techniques you want to use are pretty different than what we have in this article. But there are some pretty useful techniques! In particular, you could do attention visualization to understand where your model is looking as it predicts particular characters and optimization-based feature visualization to understand what different features in your model represent.
- AstralStorm 10y ago
  You can still have CNN hallucinate most likely set of next inputs. And the kind of activity display you did later also works.

pg_is_a_butt 10y ago

why does it such so much and constantly assume i'll write another "c" character? i don't see anything worth anything.

inlineint 10y ago

How much computing power/time did it take to train the model? Had any hyperparameters optimization been done? Sorry if I missed the reference that has this information, but I think that these numbers are important in any deep learning experiment because they allow the readers to evaluate applicability of described methods to their problems.

shancarter 10y ago
This post has more details about the model: http://blog.otoro.net/2015/12/12/handwriting-generation-demo-in-tensorflow/ http://blog.otoro.net/2015/12/12/handwriting-generation-demo...
- colah3 10y ago
  Note that the contribution of this article is techniques for visualizing models, not the novel itself.

jenniferx 10y ago

pretty cool i guess