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View Code? Open in Web Editor NEWTensorflow Implementation on Paper [ECCV2018]Semi-Supervised Deep Learning with Memory
License: MIT License
Tensorflow Implementation on Paper [ECCV2018]Semi-Supervised Deep Learning with Memory
License: MIT License
Hi, thanks for your code, it's elegant, and I learned a lot from it,
I have some questions when I read your paper,
Thank you.
Best wishes.
Thanks for sharing the code.
In the paper, the memory module is no longer needed during test. Why? why not use the predicted results of memory module?
Thanks for sharing.
I tried python3.5 to convert the images into tfrecords.
But seems the codes are not supporting python3.5.
And it works well under the condition of python2.7,
I suggest add the python version in the tutorial setting.
Thanks a lot.
Hi and thanks for the great work!
I noticed after running the train_svhn_semi.sh
script that test accuracy is always around zero percent.
The same problem happens again if I train with the supervised setting (this time prediction is always 5 for some reason).
In both case training reaches 98/99% accuracy after only 2 epochs which seems a bit strange.
I'm using tensorflow 1.9.0 and python 2.7.6
Hi,v@yanbeic Thank for the code.
While running CIFAR10 bash script I am facing the following issue:
model_checkpoint_path = ckpt.model_checkpoint_path
AttributeError: 'NoneType' object has no attribute 'model_checkpoint_path'
Could you help me out?
Hi
Thanks for the wonderful work. I found it to be a great read and very easy to understand.
(1) I am wondering what loss function did you use for the key value updates?
based on eqn. (3) it seems that mean square error loss has been utilized like
loss_k_j = sum_i=1^n_j (k_j - x_i)^2
Is there any particular reason that 1/(n_j + 1)
has been selected instead of n_j
?
(2) I am also wondering if the MND and ME loss was simply defined for the unlabeled portion of the data, how does the performance degrade ?
(3) lastly what happens if instead of updating the key and values after every epoch, we simply average out the intermediate representations and the softmax of the labeled data to re-define the key and value pairs ?
(4) Any plans to release the code in Pytorch ? I am not very familiar with tensorflow but would like to understand your method more by studying the code.
any clarifications will be helpful.
Thanks
Devraj
Hi, have you ever run your proposed model on the cifar10 dataset with fewer labeled data, eg 25 per class? I run the experiment but the test accuracy is only 10%, which is the same as the random guess.
BTW, I run the code on cifar10 with 400 labeled data per class, the test accuracy is 86.48%, which is about 1.5% lower than that in the original paper. So the running environment is ok.
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