Lesson 7 in-class chat ✅


(Ertan Dogrultan) #113

True. I was considering the entire dataset more broadly.


(Pierre Ouannes) #114

Why is that a problem ? Wouldn’t you store the model as the compression algorithm, not as part of the ouputed images ?


(jaideep v) #115

how are we producing for hi res imges for benchmarking…?


#116

gc.collect() now thats a good nugget! I’ve been restarting kernels forever!! :slight_smile:


#117

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.


(Stas Bekman) #118

And an even better version of gc.collect that does it for you automatically is: https://github.com/stas00/ipyexperiments/


FAQ, resources, and official course updates ✅
Fast.ai v3 2019课程中文版笔记
Lesson 7 Official Resources ✅
(Rohit Rawat) #119

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?


#120

Because it’s a harder task when you decouple them.


(Amit Kayal) #121

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?


(Rachel Thomas) #122

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.


(Amit Kayal) #123

OK…Thats great…so if we need significant change from original image then we should go with GAN,


(Dennis O'Brien) #125

Can the generator and critic be combined in a single network so they are trained concurrently?


(Rohit Rawat) #126

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?


(Ilia) #128

I would say that some basic understanding of GAN training process could be discovered in this PyTorch tutorial.


#129

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?


#130

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”, …


(khursani8) #131

Can Gan help in generating natural looking text(with text content based on input) like what happen to Gan generating natural looking image? If can, how to implement it?


(Alvaro) #132

Can the generator-critic pair work out images that are of the same type but different indeed to the hi-res master samples? For example a new dog in different position or with different color/fur/proportions, but that match a dog thing?


(Edward Ross) #133

Is it possible to use similar ideas to U-Net and GANs for NLP? For example if I want to tag the verbs and nouns in a sentence, or create a really good Shakespeare generator?


(Nalini) #134

Considering that a UNET is retaining the fidelity of the input all the way till the output, I would think it will provide higher accuracy even for a classifier. For example, a UNET may do a better job of classifying very similar looking cat or dog breeds. Is that not the case?

Also I wonder if perhaps UNETs could avoid bias creeps as well? For example if huskies are getting identified only because of the background snow in an image, then perhaps using a UNET over a more diverse data set would train the model not to look at background but more at the features of a husky?