I suggest you take a look at Geometric Deep Learning, but the gist here is that convolutions can be thought of as translation equivariant functions, and pooling operations as permutation invariant combinations, all operating on a graph of as many components as the number of times the operation will be output, each component is composed of the pixels that the operation will act on, so there is local information that is slowly combined through the layers, then relative positioning can be decoded when the representation is transformed into a dense 1d-vector aka flattening.

In contrast, attention mechanisms in transformers can be seen as taking into consideration dense graphs of the whole input (at least in text, I haven't really worked with vision transformers but if an attention mechanism exists then it should be similar), along with some positional encoding and a neighborhood summary.

If they indeed can be thought as stacking neighborhood summaries along with attention mechanisms, then they shouldn't fall for the same tricks since they have access to "global" information instead of disconnected components.

But take this reply with a grain of salt as I am still learning about Geometric DL. If I misunderstood something, please correct me.

Thank you for bringing up GDL. I've been following its developments, and a really great resource is this site: https://geometricdeeplearning.com/

It contains links to the paper and lectures, and the keynote by M. Bronstein is illuminating and discusses the operations on graphs that lead to equivalence to other network topologies and designs: transformer equivalence, and more.

> Keynote: https://www.youtube.com/watch?v=9cxhvQK9ALQ

How are you learning/studying GDL? Would you like someone else to discuss/learn it with?

I believe Bronstein is onto something huge here, with massive implications. Illuminating is the best adjective to describe it, as I watched this keynote:

> https://www.youtube.com/watch?v=w6Pw4MOzMuo

Everything clicked into place and I was given a new language to see the world that combined everything together well beyond the way standard DL is taught:

> we do feature extraction using this function that resembles the receptive fields of the visual cortex and then we project the dense feature representation onto multiple other vectors and pass that through stacked non-linearities, and oh by the way we have myriad of different, seemingly disconnected, architectures that we are not sure why they work, but we call it inductive bias.

> https://geometricdeeplearning.com/

That's my main source, along with the papers that lead up to the proto-book, so pretty much Bronstein's work along with related papers found using `connectedpapers.com`. I don't have an appropriate background so I am grinding through abstract algebra, geometric algebra, will then go into geometry and whatever my supervisor suggests I should read. Sure, I would like to have other people to discuss it, but don't expect much just yet.

I agree, this perspective is very interesting and tames the zoo of architectures through mathematical unification. It is indeed exciting!

Good luck with your studies/learning!

> I suggest you take a look at Geometric Deep Learning, but the gist here is that convolutions can be thought of as translation equivariant functions, and pooling operations as permutation invariant combinations, all operating on a graph of as many components as the number of times the operation will be output, each component is composed of the pixels that the operation will act on, so there is local information that is slowly combined through the layers, then relative positioning can be decoded when the representation is transformed into a dense 1d-vector aka flattening.

Hey just trying to check my understanding, this is what Taco-tron does right? It outputs an image that can be Fourier transformed into a soundwave, which is the flatting to a dense 1d vector? And the construction of that image works because the network was able to learn from examples of existing sounds transformed into images, because the transformation into an image encodes some invariance that biases the learning network to generalize better or something?

I didn't do really great in math in college, but always found deep learning interesting. Not sure if anything I said above makes any sense.

Since it easy to fool people with optical illusions I doubt that you will be able to train a computer to not be fooled by optical illusions.

The hard thing to emulate is that people quickly become aware that they are looking at an illusion. Even though you can't turn your perception off, Escher's infinite staircase doesn't actually trick you into thinking a set of stairs can go in a closed loop.

It fools humans only at the first glance. A few seconds later we make a correct assessement.

Typical CNNs miss this second stage.

And the humor of the images comes from our initial expectations and how different they are from our actual understanding of what we're seeing.

I think because the human world is made for humans there’s a lot of value in an AI with similar failure modes to humans. Right now AI can do a good job of learning to classify images, but fails in ways that are entirely foreign to us

We can use our understanding of how the world usually is and plausibly might be to get us out of local minima.

Those pictures are definitely NSFW when viewed at low res/from far away, which is how coworkers typically see your monitor contents. An argument that starts with “Well, technically” is unlikely to carry much weight in a discussion with HR (and probably rightfully so).

I think the scientific point here is that visual processing is not a one-shot process. Tasked with object detection, some scenes demand more careful processing and more computation.

Almost all neural network architectures process a given input size in the same amount of time, and some applications and datasets would benefit from an "anytime" approach, where the output is gradually refined given more time.

I understand the point you are making, but it's kind of irrelevant. The task is to produce an answer for the image at the given resolution. It is an accident and coincidence that the neural network produces an answer that is arguably correct for a blurrier version of the image.

Counter argument in addition to downvote, please?

Probably because "Well, technically" is a really unfair way to characterize that argument.

It's not porn. It's not simulated porn. It's a hallway, and if you're not setting it as your desktop background to trick people on purpose then you're not doing anything wrong.

Not sure if this is along the lines of what you're thinking, but we tried looking at this a while ago: https://arxiv.org/abs/2103.14586