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Feature Visualization: How neural nets build up their understanding of images

distill.pub

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Re: Feature Visualization: How neural nets build up their understanding of images

#31
post #14

Wow. That's incredible how psychedelic these images are. I'd be really curious to learn more about the link between these two seemingly distant subjects.

Brain and NNs, at least in the first visual cortex layers and the first perception layers accordingly, are doing the same thing - building optimal sparse coding for the body of imagery they've been exposed too (in general it isn't limited to images, it is just that our knowledge for image processing seems to be a bit more advanced). There are works from 199x proving that Gabor kernels - which form the first layer in the visual cortex and which (or very similar) happen to emerge in the first layers of well trained NNs too - are optimal for a wide class of image inputs (edge density, etc.), basically the class of images naturally surrounding us. That optimality provides for a good explanation of their emergence in NN.

It seems to be a natural speculation that the further layers of visual cortex/brain and NNs are subject to similar optimization, and thus they would have similar kernels. I think psychedelics somehow stimulate these deep layers to massively fire without waiting for the normally required input from the eyes to propagate to those layers to selectively fire neurons in them.

Re: Feature Visualization: How neural nets build up their understanding of images

#32

Is there any way to run images from a camera real-time into GoogLeNet? E.g. like if I want to scan areas around me to see if there are any perspectives in my environment that light up the "snake" neurons or the dog neurons???

The Jetson TX2 can run GoogLeNet in real-time with the onboard camera, so it's definitely possible on mainstream GPUs too.

https://github.com/dusty-nv/jetson-inference

Inspecting layer activations in real-time is trickier, but presumably possible.

Re: Feature Visualization: How neural nets build up their understanding of images

#33
post #16

Looking at the finger instead of the moon: I like the HTML layout (responsive, inline images with captions, lateral notes). Any insights on how it's generated? Markdown, Rst, Latex -> HTML? I would love to produce my documentation in this way. Edit: I was too hurried. Everything is explained in https://distill.pub/guide/ , the template is at https://github.com/distillpub/template

Looking at the finger instead of the moon

I like this metaphor.

Re: Feature Visualization: How neural nets build up their understanding of images

#34
Great presentation, but I do wish they'd throw in an equation or two. When they talk about the "channel objective", which they describe as "layer_n[:,:,z]", do they mean they are finding parameters that maximize the sum of the activations of RGB values of each channel? I'm not quite sure what the scalar loss function actually is here. I'm assuming some mean. (They discuss a few reduction operators, L_inf, L_2, in the preconditioning part but I don't think it's the same thing?)

The visualizations of image gradients was really fascinating, I never really thought about plotting the gradient of each pixel channel as an image. I take it these gradients are for a particular (and same) random starting value and step size? It's not totally clear.

(I have to say, "second-to-last figure.." again.. cool presentation but being able to say "figure 9" or whatever would be nice. Not everything about traditional publication needs to be thrown out the window.. figure and section numbers are useful for discussion!)

Re: Feature Visualization: How neural nets build up their understanding of images

#35
post #16

Looking at the finger instead of the moon: I like the HTML layout (responsive, inline images with captions, lateral notes). Any insights on how it's generated? Markdown, Rst, Latex -> HTML? I would love to produce my documentation in this way. Edit: I was too hurried. Everything is explained in https://distill.pub/guide/ , the template is at https://github.com/distillpub/template

Looking at the finger instead of the moon I like this metaphor.

(In case "I like this metaphor" isn't a comment about how you have liked and continue to like it, it's a reference to an old Buddhist saying. I could have sworn it also appeared on the first page of the Tao Te Ching, but I guess I had just remembered the gist.)

Re: Feature Visualization: How neural nets build up their understanding of images

#36
post #24
post #23

Earlier quoted context omitted.

We're glad you enjoyed it! :D > do you think it would be possible to train a DNN to learn to visualize the "most important" neuron activations / interactions of another DNN? That sounds like a really hard problem. I'm not entirely sure what it would mean even, but it would not surprise me at all if there was some refinement that could turn into an interesting research direction! :)

Thanks. I asked the question in such an open-ended way just to see if you had any crazy ideas. It does sound like a hard problem. In terms of what it could mean, one idea I just had is to take a trained model, randomly remove (e.g., zero out) neurons, and then train a second model to predict how well the trained model continues to work without those removed neurons. The goal would not be to 'thin out' the first model…

https://blog.acolyer.org/2017/11/01/deepxplore-automated-whi...

Check this out

Re: Feature Visualization: How neural nets build up their understanding of images

#38

Great presentation, but I do wish they'd throw in an equation or two. When they talk about the "channel objective", which they describe as "layer_n[:,:,z]", do they mean they are finding parameters that maximize the sum of the activations of RGB values of each channel? I'm not quite sure what the scalar loss function actually is here. I'm assuming some mean. (They discuss a few reduction operators, L_inf, L_2, in the…

[deleted]

Re: Feature Visualization: How neural nets build up their understanding of images

#40
This pictures reminds me about what one's can see under psychedelics. All sensory input basically begins to break down to that kind of patterns, and thus reality dissolves into nothing. This is equally terrifying and liberating depends on look. The terrifying thought is that there's no-one behind this eyes and ears. The liberating thought is that if there's no-one there, then there's no-one to die.
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