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For this lab, you should implement the style transfer algorithm referenced above. We are providing the following: | For this lab, you should implement the style transfer algorithm referenced above. We are providing the following: | ||
- | - [[http://liftothers.org/byu/lab10_scaffold.py|Lab 10 scaffolding code]] | + | - [[http://liftothers.org/byu/lab10_scaffold.py.txt|Lab 10 scaffolding code]] |
- | - [[http://liftothers.org/byu/vgg16.py|The VGG16 model]]\ | + | - [[http://liftothers.org/byu/vgg16.py.txt|The VGG16 model]] |
- [[http://liftothers.org/byu/vgg16_weights.npz|VGG16 weights]] | - [[http://liftothers.org/byu/vgg16_weights.npz|VGG16 weights]] | ||
- [[http://liftothers.org/byu/content.png|An example content image]] | - [[http://liftothers.org/byu/content.png|An example content image]] | ||
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I found that it was important to clip pixel values to be in [0,255]. To do that, every 100 iterations I extracted the image, clipped it, and then assigned it back in. | I found that it was important to clip pixel values to be in [0,255]. To do that, every 100 iterations I extracted the image, clipped it, and then assigned it back in. | ||
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+ | ...although now that I think about it, perhaps I should have been operating on whitened images from the beginning! You should probably try that. | ||
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+ | ---- | ||
+ | ====Bonus:==== | ||
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+ | There's no official extra credit for this lab, but have some fun with it! Try different content and different styles. See if you can get nicer, higher resolution images out of it. | ||
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+ | Also, take a look at the vgg16.py code. What happens if you swap out max pooling for average pooling? | ||
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+ | What difference does whitening the input images make? | ||
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+ | Show me the awesome results you can generate! | ||
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