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discont's Issues

Collection of transformed chunk data

Dear Sarthak,

Many thanks for the excellent paper and publishing your code on GitHub. Much appreciated!

I have a question regarding the collection of the transformed chunks in line 164 of the training loop contained in the train.py module. Shouldn't it read:

transformed_chunks[i * FLAGS.z_num_chunks : (i+1) * FLAGS.z_num_chunks] = curr_cv_full_view

instead of:

transformed_chunks[i*FLAGS.z_num_chunks : (i+1)*FLAGS.z_num_chunks]

to collect the distinct transformed chunks and to subsequently compute the center loss? I fear that otherwise, the information contained in the transformed_chunks tensor won't contain any updated data other than the initialisation.

Thanks again,
Marco

Question about the way to update context vector

Hello Sarthak268,

Thanks for sharing your code. The idea is great.

I checked the code and I have a question,

You create variable cv_full_view:

cv_full_view = Variable(torch.zeros((FLAGS.z_num_chunks, FLAGS.c_num_chunks*FLAGS.c_chunk_size)))

But somehow you overwrite it:
cv, cv_full_view = cv_network(specified_latents)

If I'm not wrong, I think the idea is to use the stored cv_full_view (centers) to get the center_loss, update it, then keep doing this procedure, right? Otherwise, there is no point to do:

cv_full_view = cv_full_view - FLAGS.center_loss_lrate*cv_gradient # CV update

cause you get new cv_full_view every iteration.

Thanks again! Great paper!

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