Because you don’t want o break the pretrained weights.
2 Likes
the critic model is a supervised one is it? where the classification (real/generated) label is marked a priori?
you can say any ML model is a way of doing compression
1 Like
Except they often are more weights than the entire dataset they want to compress 
3 Likes
True. I was considering the entire dataset more broadly.
Why is that a problem ? Wouldn’t you store the model as the compression algorithm, not as part of the ouputed images ?
how are we producing for hi res imges for benchmarking…?
gc.collect() now thats a good nugget! I’ve been restarting kernels forever!! 
8 Likes
I guess your problem would be compute afterward, you can’t require a GPU each time you want to decompress your image, with a software that is super heavy.
And an even better version of gc.collect that does it for you automatically is: https://github.com/stas00/ipyexperiments/
10 Likes
I thought it compares pairs of images: an original hi-res with it’s corresponding predicted. It seems the discriminator is a regular 2 class image classifier with no knowledge of image pairs. Why don’t we compare them pairwise?
Because it’s a harder task when you decouple them.
1 Like
GAN helps us to generate new images from existing ones and same we can do with Image augmentation? Wanted to know how they both are different?
Are you asking how the GAN (which includes Generator & Discriminator) is better than just using the Generator? The Generator can end up learning to create images that are different in significant ways from what we want (in this case, our high-res input), but the GAN addresses this.
1 Like
OK…Thats great…so if we need significant change from original image then we should go with GAN,
Can the generator and critic be combined in a single network so they are trained concurrently?
1 Like
If we compared the blinded hi-res cat to the original hi-res cat, maybe the generator would see a difference and try to fix it? Maybe the problem is that it looks at each image by itself to judge its hi-res-ness?
I would say that some basic understanding of GAN training process could be discovered in this PyTorch tutorial.
9 Likes
If UNets are well-suited for cases when the output resolution is like the input resolution, then are they likely to be the right architecture when you want to train a model to identify a specific location in an image rather than a specific object?
Or would it be wiser to train a regressor to predict the location as coordinates in the image?
2 Likes
Would GANs work for text classification? I.e., use the critic to classify text as “spam” vs “not spam”, and the generator to generate increasingly sophisticated spam text?
I am thinking of classifying emails for example - where spammers can create multiple variations of stuff like “viagra”, “v1agra”, “v iag ra”, …
5 Likes