Recently Google publicized interesting deep dream. Besides art generation such as http://deepdreamgenerator.com/, do you see any potential applications of deep dream in computer vision or machine learning?
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There's already at least one application out, if you interpret 'application' broadly enough: Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation by Hong, Noh and Han. They use it for image segmentation. Standard image recognition networks can only give you a bounding box for each object recognized on an image. If you want to know which pixels constitute that object, you have to do image segmentation.
Basically, after finding a dog on an image, Hong et al's architecture back-propagates the dog-ness through the neural network down to the pixel level, to find the pixels that were the most responsible for the dog appearing. (They then use this heatmap as input for a supervised segmentation network, there's no deep dreaming in that part.)
This is already kind of an existence proof that the Deep Dream idea can be useful outside image manipulation. But I wouldn't downplay image manipulation itself either. I mention two things that are not immediate applications of Deep Dreaming, and we don't have them currently, but I can kinda see a plausible road from the original Deep Dream algorithm towards these:
- Beautifying pictures and human faces and bodies. (Automating what a Photoshop retouch artist does.)
- CSI-style image upscaling with fake but believable interpolated detail.
Dániel Varga
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heres another application that is very new & just demonstrated within last few weeks. computers are filtering images to look like paintings in the distinctive style of different artists eg Van Gogh, Picasso, etc... and it seems possible since the technology can encompass different artistic styles it might be used for forgery detection in the art world at some point. (many very advanced analysis techniques are used in this area historically.) note that filtering methods are very popular on Instagram so it seems likely these will be commercially available at some point.
vzn
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It is impossible to prove a negative, but other than using the same pattern detection system in general to detect shapes/images and replace them with other similar images, possibly for use in automatic image correction or similar, I don't think it has real potential outside of modifying pictures.
I may have to delete this answer if it is proven wrong.
DoubleDouble
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Greyscale to Color
For example:
http://s15.postimg.org/3xq8jx03f/image.jpg
to
http://s15.postimg.org/i5fx8kcsb/image.jpg
http://s15.postimg.org/c5s64wrzv/image.jpg
The tree wood seems unnaturally red but still, it's not bad. This has worked but less impressively with other greyscale images I've tried.
alan2here
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Naritivly context aware, visual profanity filter.
In other worlds, rendering physically realistic and thematically/stylistically appropriate clothes on people who are insufficiently dressed, to make the image more family safe.
Thats the idea, however at the moment it's both unreliable and when it does work inaccurate.
However, more tweaking of the parameters of the dream than I have access to, or possibly just using more iterations and a lower "octave" value than I can specify should make the results much more reliable.
Examples:
Before: http://s22.postimg.org/5sjpqbzoh/image.jpg
After: http://s22.postimg.org/wew6fb3vl/image.jpg
.
alan2here
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