Comment by YeGoblynQueenne | Hacker News Reader

Comment by YeGoblynQueenne | Hacker News Reader

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YeGoblynQueenne

>>I then pulled out the Google Translate app from my pocket, put it in airplane mode, and demonstrated how it translates the text under the camera directly on the image.

So I had a look at the google reseach blog cited [1] and it turns out that Deep Learning is not used to do translation in the Google Translate app. It's only used to do Optical Character Recognition- OCR. Note, that's not handwritten digit recognition, I haven't seen any claims that the Google Translate app can do that, so it most probably can't, and can only recognise print characters.

OCR Is not something you absolutely need a deep network to do, in fact it's one of those cases were you really don't want to deploy such an expensive system because there are far cheaper alternatives, like the logistic regression mentioned in the article.

The google research blog typically doesn't say anything about how the actual translation is done, but it seems to me, from playing around with the app a bit, that it does word-for-word translation, possibly with some probabilistic heuristic to figure out the most common/likely such translation. That's very reasonable, given the app has to run on possibly weak hardware but it's also not as marketable as "Machine Translation with Deep Learning on Google Translate App".

So this is not a very good example of the superiority of Deep Learning. It's more a good example of the superiority of the Google hype machine.

[1] http://googleresearch.blogspot.co.uk/2015/07/how-google-tran...

Google Translate uses the CNN to extract the text from the image. That's much harder than usual OCR.

It's more a good example of the superiority of the Google hype machine.

That's pretty unfair. They managed to get this working on a mobile, which is where the real work was done.

I totally agree that it's a delightful engineering feat to get all that to run on a smartphone and I'm suitably impressed actually.

The hype I'm concerned about is the one about Deep Learning. The article is careless with the technology details and makes it sound as if Google is running deep neural networks for machine translation on peoples' phones, which would be, at this time, bigger news than AlphaGo beating Lee Sedol.

Running a DNN on a phone isn't super hard. Training one is, but running is pretty efficient.

They definitely could run a DNN for translation on phones. For example the example seq2seq implementation in TensorFlow[1] would run on a phone fine, and that achieves close to state-of-the-art.

Google uses a DNN for offline speech recognition.

[1] https://www.tensorflow.org/versions/r0.8/tutorials/seq2seq/i...

For example the example seq2seq implementation in TensorFlow[1] would run on a phone fine, and that achieves close to state-of-the-art.

Lol no it wouldn't and it doesn't. Either you would have to strip it down too much to fit on a phone, so it would lose performance (and it's far from state-of-the-art anyway, being an example) or it would not fit on your phone, unless you removed everything else from it (and even then).

Just to put things into perspective: there are large swaths of the global population that do not even own smart phones. They need translation also, you know.

no it wouldn't and it doesn't. Either you would have to strip it down too much to fit on a phone, so it would lose performance (and it's far from state-of-the-art anyway, being an example) or it would not fit on your phone, unless you removed everything else from it (and even then).

Yes, it is an example program. Nevertheless, to quote: "Even if you want to transform a sequence to a tree, for example to generate a parsing tree, the same model as above can give state-of-the-art results, as demonstrated in Vinyals & Kaiser et al., 2014"

Language modelling isn't the same as translation, but it is closely related. Seq2seq as an approach does achieve close to state-of-the-art for translation[1].

Why do you think it won't fit on your phone?

A related seq2seq model in TensorFlow comes in at less than 500kb[2]. The (much more complex) Image Recognition demo is designed to run on Android[3].

The new TensorFlow quantization approach[4] will reduce that size and increase performance even more.

[1] http://arxiv.org/pdf/1409.3215.pdf

[2] https://github.com/cmusphinx/g2p-seq2seq

[3] https://github.com/tensorflow/tensorflow/tree/master/tensorf...

[4] https://petewarden.com/2016/05/03/how-to-quantize-neural-net...

>> Yes, it is an example program.

Indeed. I guess I thought you were saying that this tutorial example could achieve state of the art results. Apologies for the misunderstanding.

>> Why do you think it won't fit on your phone?

The question is really why do you think it will. You claim it does. You need to show that's true. Can you show me a trained machine translation system using deep neural networks running on a smartphone and having state of the art performance?

Until you do (or anyone does) I will remain skeptical. As I should and as should everyone. You can't expect to just claim state of the art performance and have everyone praise your achievement.

>> The (much more complex) Image Recognition demo is designed to run on Android[3].

That's a demo. I guess it might be state of the art in demos?

Regardless, here's a hint of how practical it is to deploy this demo on users' devices. This bit I quote from the demo's github:

The TensorFlow GraphDef that contains the model definition and weights is not packaged in the repo because of its size.

So maybe it will run on your phone but good luck sending that to your users. Unless you want them at your castle gates with torches and pitchforks.

>> The new TensorFlow quantization approach[4] will reduce that size and increase performance even more.

Great news. I'm at the edge of my seat.

What do you mean by expensive? Smaller neural networks are computationally cheap to run forward propagation on, which is all you need to do after deployment. This model is running in real time on your phone, after all. I'm also confused by your suggestion to use logistic regression instead. The accuracy will certainly be worse - even if you use relatively state of the art non-convolutional features like a spatial pyramid with SIFT or HOG (the computation of these features would almost certainly be more expensive than running forward propagation for a reasonably sized neural network, in any case). Thoroughly confused over here.

Both bits you're confused about, how deep nets are expensive to run and logistic regression scales better etc, are mentioned in the article and I use them in the same context.

EDIT: can't wait for the post timer to cool down, so please keep in mind I mean the submitted article, not the google research blog cited in that article.

how deep nets are expensive to run

I think you are overstating what the article says here. Google Translate lets you view an image through your phone's camera and it replaces - in real time - words in one language with the translated version in another. It's pretty amazing, and to get it to work the whole stack needs to be optimized. Part of that is making the network as efficient as possible, and one way to do that is to make it smaller.

logistic regression scales better etc, are mentioned in the article

I'm a bit confused. Neither of the words "logistic" or "regression" are mentioned in the article you linked to (http://googleresearch.blogspot.com.au/2015/07/how-google-tra...).

>> I'm a bit confused. Neither of the words "logistic" or "regression" are mentioned in the article you linked to

As noted above, logistic regression as a more scalable alternative to deep neural networks is mentioned in the parent article, not the link to the google research blog. The google research blog is linked to from the parent article as proof of the parent article's claim that deep nets do machine translation on your phone.

You do need deep-learning to get state-of-the-art OCR when the image is not scanned from a book, but photographed text (or text-in-the-wild) in unconstrained conditions.

It is a good example of the superiority of deep learning.

One recent example: Reading Text in the Wild with Convolutional Neural Networks. Jaderberg et al. 2016. https://www.robots.ox.ac.uk/~vgg/publications/2016/Jaderberg...

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