Lecture 6: Image Generation using Generative Adversarial Networks (GANs)

Lecture Date and Time: January 2, 2021
English: 9 PM IST/8.30 AM PST | Add to calendar (Google)
Hindi: 11 AM IST | Add to calendar (Google)

Asking/Answering Questions :
Reply on this thread to ask questions during and after the lecture. Before asking, scroll through the thread and check if your question (or a similar one) is already present. If yes, just like it. During the lecture, we’ll answer 8-10 questions with the most likes. The rest will be answered on the forum. If you see a question you know the answer to, please post your answer as a reply to that question. Let’s help each other learn!

No lecture today? Am I missing something?

What are the business use cases for GANs, animeface generation seems to be a fun exercise :slight_smile:


It was postponed to 2nd January due to the holidays, hope you didn’t miss our email and were able to attend it :v:


please are we writing stats = (0.5, 0.5, 0.5), (0.5, 0.5, 0.5) because we have 3 channels and we want the mean and the standart deviation in each channel to be equal to 0.5?

Also, I didn’t really understand what T.CenterCrop(image_size) do exactly?
And what *stats means here : T.Normalize(*stats) ?

Also, I didn’t understand why the implementation of this function is like that ?
def denorm(img_tensors):
return img_tensors * stats[1][0] + stats[0][0]

Thank you !