sarthak268 / discont Goto Github PK
View Code? Open in Web Editor NEWPyTorch Implementation for the paper "DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors" (ECCVW'20).
License: MIT License
PyTorch Implementation for the paper "DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors" (ECCVW'20).
License: MIT License
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:
Line 110 in a1b6d75
Line 149 in a1b6d75
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:
Line 185 in a1b6d75
Thanks again! Great paper!
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
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