pmorerio / minimal-entropy-correlation-alignment Goto Github PK

Hello, @pmorerio

Thank you for your nice work!

I got a question about calculating log_coral_loss.

Let say, activation after conv_layer is the size of [20, 256, 200, 176] (N, H, W, C respectively), it is too big to flatten and calculate the covariance matrix.

In this case, what can be a good solution?
(1x1 convolution and 2d_maxpool would work properly...?)
Do you have a similar experience?

Any advice and comments are welcome!

Thank you in advance.

Hi @pmorerio ,

I have three questions regarding the log_coral_loss:

It doesn't work with batch size 0 why is there no workaround? Is that part of the method?
Is the cov result supposed to be complex?
Did you try yet to apply this method to latent spaces? Or are you aware of anyone doing it?

Best,
Bjarne

Hello, @pmorerio

I got another question about the eigenvalue of cov_matrix.

As far as I understood from your paper, cov_representation is positive definite, therefore
taking a logarithm on the eigenvalues makes sense.

However, when I decompose my covariance matrices I got some negative eigenvalues which result in nan logarithm values.

1. Is this expected behavior?

2. If so, coral_loss can perform as well as log_coral_loss?

I am afraid that I miss some part of your paper.
Any comments are welcome!!

Best,
Yoo

In your paper you mentioned that "The architecture is the very same employed in [Ganin & Lempitsky (2015)] with the only difference that the last fully connected layer (fc2) has only 64 units instead of 2048. Performances are the same, but covariance computation is less onerous. fc2 is in fact the layer where domain adaptation i performed." But in your code I found that (may be I am wrong), fc2 has 128 units. Can you please explain here a little bit more to understand me please?
hidden_size = 128
self.hidden_repr_size = hidden_size

net = slim.fully_connected(net, self.hidden_repr_size, activation_fn=tf.tanh,scope='fc4')

Hi ! I have re-run your code and got 90% accuracy for both log-d-coral and d-coral. Is there any missing in the code ? I just use following command:
for log-d-coral:
python main.py --mode='train' --method='log-d-coral' --alpha=1. --device='/gpu:0'
python main.py --mode='test' --method='log-d-coral' --alpha=1. --device='/gpu:0'

for d-coral:
python main.py --mode='train' --method='d-coral' --alpha=1. --device='/gpu:0'
python main.py --mode='test' --method='d-coral' --alpha=1. --device='/gpu:0'