101–110 of 166 posts
Does anyone have a good sense of what exactly they mean here: >Instead of exactly prescribing which feature we want the network to amplify, we can also let the network make that decision. In this case we simply feed the network an arbitrary image or photo and let the network analyze the picture. We then pick a layer and ask the network to enhance whatever it detected. Each layer of the network deals with features at…
Has anyone seen an explanation for the why the images end up with that colour palette?
Kinda make me contemplate more own conscious experience :)
Does anyone have a good sense of what exactly they mean here: >Instead of exactly prescribing which feature we want the network to amplify, we can also let the network make that decision. In this case we simply feed the network an arbitrary image or photo and let the network analyze the picture. We then pick a layer and ask the network to enhance whatever it detected. Each layer of the network deals with features at…
They say: oh you think that cloud is a tiny bit dog-like? Ok, well then find me a small modification to the image that would make it a little more dog like, then a little more, and so on.
Think of it as semantic contrast enhancement
I wonder if the engineers at Google can make the same experiment with audio... It'll be funny to listen the results.
https://medium.com/@stripenight/seeing-how-computers-might-t...
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tldr: To figure out how computers "think", Google asked one of its artificial intelligence algorithms to look at clouds and draw the things it saw!
There's this complex Artificial Intelligence algorithm called a neural network ( https://en.wikipedia.org/wiki/Artificial_neural_network ). It's essentially code which tries to simulate the neurons in a brain.
Over the last few years, there have been some really cool results, like using neural networks to read people's handwriting, or to figure what objects are in a picture.
To start your neural network, you give it a bunch of pictures of dogs, and tell it that those pictures contain dogs. Then you give it pictures of airplanes, and say those are airplanes, etc. Like a child learning for the first time, the neural network updates its neurons to recognize what makes up a dog or an airplane.
Afterwords, you can give it a picture and ask if the pic contains a dog or an airplane.
The problem is that WE DON'T NOW HOW IT KNOWS! It could be using the shape of a dog, or the color, or the distance between it's legs. We don't know! We just can't see what the neurons are doing. Like a brain, we don't quite know how it recognize things.
Google had a big neural network to figure out what's in an image, and they wanted to know what it did. So, they gave the neural net a picture, but stopped the neural net at different points, before it could finish deciding. When, they stopped it, they asked it to "enhance" what is just recognized. Eg. if it just saw the outline of a dog, the net would return the picture with the outline a bit thicker. Or, if it saw the colors similar to a banana, it would return the picture with those colors looking more like a banana's colors.
This seems like a simple idea, but it's actually really complex, and really insightful! Amazing images here - https://photos.google.com/share/AF1QipPX0SCl7OzWilt9LnuQliat...
Original article - http://googleresearch.blogspot.com/2015/06/inceptionism-goin...
Does anyone have a good sense of what exactly they mean here: >Instead of exactly prescribing which feature we want the network to amplify, we can also let the network make that decision. In this case we simply feed the network an arbitrary image or photo and let the network analyze the picture. We then pick a layer and ask the network to enhance whatever it detected. Each layer of the network deals with features at…
The detection layer will detect very faint random signals. For example, if you have a unit that's supposed to detect dogs, it might be very faintly activated if by random chance there is a doggish quality to some part of the image. What they do is pick up that faint, random, signal and amplify it. They say: oh you think that cloud is a tiny bit dog-like? Ok, well then find me a small modification to the image that wo…
This is one of the most astounding things I've ever seen. Some of these images look positively like art. And not just art, but good art.
Makes me wonder what passes as good art nowadays. But yeah some of the renderings were particularly aesthetic.
Am I the only person who is not entirely happy about the overuse of the pop-culture term 'inception' for everything that is remotely nested, recursive or strange-loop-like? In this paper, we will focus on an efficient deep neural network architecture for computer vision, codenamed Inception, which derives its name from the Network in network paper by Lin et al [12] in conjunction with the famous “we need to go deeper…
Earlier quoted context omitted.
The detection layer will detect very faint random signals. For example, if you have a unit that's supposed to detect dogs, it might be very faintly activated if by random chance there is a doggish quality to some part of the image. What they do is pick up that faint, random, signal and amplify it. They say: oh you think that cloud is a tiny bit dog-like? Ok, well then find me a small modification to the image that wo…
So in concrete terms, does this mean that we show the network an image, choose one layer's output vector, and then back-propagate gradients to the image such that the direction of that vector stays the same, but the magnitude increases?
That plus a prior on the input pixels to keep it image-like.