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Cool demo but I still wonder if fundamentally this is just a brute-force approach. Wouldn't it be better to do some traditional preprocessing (e.g. recognizing rectangles, circles, etc.) and feeding higher-level descriptors into the classifier?

If the net learns based on pixels you still have to somehow solve rotation and scale invariance. Or is there something new in deep-learning vs. old-school neural nets that fixes the issues that bedeviled neural nets the first time they were popular?



I think they used the methods described in http://www.cs.berkeley.edu/~rbg/papers/r-cnn-cvpr.pdf


Thanks, interesting paper.




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