
Drilling Down on Depth Sensing and Deep Learning - jonbaer
https://bair.berkeley.edu/blog/2018/10/23/depth-sensing/
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stefan_
Why are structured light sensors not used instead of color camera images?
Because in sunlight it becomes impossible to see your own projected IR light.
So they stop working.

And projecting a 3D 360 degrees point cloud from a LIDAR to a 2D image is
weird. Why take that loss when the algorithms can just work on the point
cloud.

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YeGoblynQueenne
>> This class of low-cost depth sensors emerged just as Deep Learning boosted
Artificial Intelligence by accelerating performance of hyper-parametric
function approximators leading to surprising advances in image classification,
speech recognition, and language translation.

So I haven't really looked at machine translation benchmarks for a while, but
the paper linked by the article itself (as an example of the "surprising
advances" in language translation) reports results below the state of the art
(from 2014) (see [1] below).

The system noted as the state of the art, [2], is a phrase-based statistical
machine translation system, which basically boils down to n-grams, with a few
tricks up their sleeves such as a 5-gram operation sequence model,
hierarchical lexicalised reordering, an unsupervised transliteration model
(for translations between Hindi and Urdu) etc etc.

This is interesting to me because I hear this claim, that deep learning has
advanced machine translation significantly, very often- but when I look for
results, I can't really find anything that supports that. It's obvious that
machine translation research currently involves a lot of deep learning, but
whether that is actually working, or it's just what people are trying at the
moment to see if it works, I can't tell.

It certainly doesn't look -to me at least- like we've seen anything like the
big leap on ImageNET and CIFAR results, when it comes to machine translation.
The paper linked by the article is a good example: their baseline is a 33.30
BLEU score and their system raises that to 34.81, an improvement of 1.51, a
less than 5% increase (on the previous result). And that's only on English-to-
French (and still behind the top results).

I really don't see the "surprising advancements" here.

______________

[1] [https://papers.nips.cc/paper/5346-sequence-to-sequence-
learn...](https://papers.nips.cc/paper/5346-sequence-to-sequence-learning-
with-neural-networks.pdf)

[2]
[https://www.aclweb.org/anthology/W14-3309](https://www.aclweb.org/anthology/W14-3309)

