Comment by is74
13 years ago
Actually, it does, since the difference in performance between entry #1 and entry #2 is so huge (25% error vs 15% error!), and since this is by far the hardest computer vision challenge yet!
13 years ago
Actually, it does, since the difference in performance between entry #1 and entry #2 is so huge (25% error vs 15% error!), and since this is by far the hardest computer vision challenge yet!
Sorry for disagree, but it seems more related to the fact that they are using deep convolutional learning rather than the neural network itself. If you use an ANN with the same set of features side by side with a SVM you will see very equivalent results.
I will be more agree with a title like "Deep Convolutional learning overperformed traditional techniques in Object Recognition"
Yeah, if you use the same raw RGB features for the SVM as the neural net then the neural net would blow the SVMs away even more utterly.
No... but I'd bet that if you use the high dimensional features resulted from the deep convolutional learning process as an input of an SVM the difference would not be that significant.
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