
Capsule Networks- the New Deep Learning Network - joeyespo
https://towardsdatascience.com/capsule-networks-the-new-deep-learning-network-bd917e6818e8
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stared
Aren't Capsule Networks one of this hyped ideas - everyone was talking about
them (because their rationale sounded interesting, and well, Hinton) were
never seen again?

It is very different to _good_ ideas, like 3x3 conv, dropout, batch norm,
residual connections - which once introduced, become standard building block
of networks.

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MAXPOOL
They are not hyped. Capsule nets are in early phase of research.

The bleeding edge deep learning research that tries to push the science
forward is trying to develop new ideas. Hinton et. al developed the current
deep learning revolution gradually over decades. Next non-incremental
revolutions in the field may take similar time to mature.

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heyitsguay
Oh for sure! Not everybody needs to be trying to max out the standard
benchmarks with every paper. But have there been _any_ new developments or
improvements with capsule nets since the original paper? This blog post looks
like it only references that first one, which is coming up on two years old,
right? It'd be great to read about any followup.

~~~
stochastic_monk
Graph Capsule Convolutional Networks [0] claim to have beaten the state of the
art in graph neural networks. However, I can’t find information on training
time, which I think is significantly more expensive for capsule networks.

[0] [https://arxiv.org/abs/1805.08090](https://arxiv.org/abs/1805.08090)

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amelius
> but because CNN’s only look for features in images, and don’t pay attention
> to their pose, it’s hard for them to notice a difference between that face
> and a real face.

Then this makes me wonder: how are GANs able to create pictures of people
where eyes and noses and so on are correctly placed with respect to eachother?

E.g. as in: [https://www.theverge.com/2018/12/17/18144356/ai-image-
genera...](https://www.theverge.com/2018/12/17/18144356/ai-image-generation-
fake-faces-people-nvidia-generative-adversarial-networks-gans)

~~~
swframe2
GANs are a battle between the discriminator and the generator. If the G places
eyes in the wrong place, the D and G eventually get error signals that cause
them to improve. Using pooling layers, CNNs build features that look across
the entire image. So it can learn that eyes need to go across each other and
above the nose. It is just that when used for classification, CNNs may not
need to learn this because it is never trained to discriminate real and fake
faces.

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paradoxparalax
I am a fan of giving new nicknames to things when I do any noticeable, even
collateral, in extremely early stage, improving.

Financially wise, must be a good thing to do, too, but it is not the main
point. The main point is Not that I sound smarter when I use the new
nickname(therefore this is not exactly sarcasm), but that I can have a more-
at-hand/shorter way to identify the new, subtly improved version, and It saves
a lot of words, in the "spoken". colloquial, communication.

Now for the matters of Science improvement itself, I mean, like for Humanity
and stuff, I am not clear if this is good, maybe. Science and business, in a
very subtle, almost invisible way, mixed. Always have been like this, and it
probably will continue like this, for a while.

