Thanks for wonderful lecture.
I have some questions about super resolution of the paper "Perceptual Losses for Real-Time Style Transfer and Super-Resolution".
1 : Where could I find "trn_resized_72_r.bc" and "trn_resized_288_r.bc"?Are they part of the imagenet data set provided in the torrent link of lesson 9?
2 : I find out the axis of K.mean is difficult to understand
dims = list(range(1,K.ndim(diff)))
return K.expand_dims(K.sqrt(K.mean(diff**2, dims)), 0)
#result of this line is same as K.mean(diff), but K.mean(diff, dims) return an array
I do some experiments on numpy, the results confuse me(I think K.mean is similar to numpy.mean)
shape = (1,2,2,2)
cimg = np.arange(8)
cimg = cimg.reshape(shape)
contents of cimg
[[ 3. 4.]]
What is going on?
3 : In the video of lesson 9, why the target is
targ = np.zeros((arr_hr.shape, 128))
targ = np.zeros((arr_hr.shape, 144, 144, 128))
Answer : I find out why, because the lost function output 128 loss value.Why 128 but not 1?Maybe 128 is more discriminate. When using 3 output layers, we make the output as single value, because this is easier to calculate.
Edit : By the way, I saw some interesting videos on youtube
Enhance! Super Resolution From Google | Two Minute Papers #124
What is Google RAISR? Google RAISR Software | Smart Upsampling of Photos
I do not know how good they are compare with the super resolution with perceptual loss, I am still trying to understand lesson 9, if everyone know how good/bad it is, please share some comments, thanks