Learning of kernels about e2cnn HOT 2 CLOSED

Hi @danushv07

I'm happy you liked our work :)

The attribute weights stores the learnable parameters while filter stores the expanded convolution filter.

In the forward pass, the learnable weights are used to construct the convolutional filters which are then used in a standard conv2d. Yes, by backpropagation, the gradient is then passed through the built filters to the learnable weights.
Since the weights are updated at each iteration, the convolutional filter need to be re-generated every time during training.
At test time, we only generate the filter once and store it in the filter attribute.

See also this issue #2 if you are having issues with storing / loading the weights of a model.

Best,
Gabriele

from e2cnn.

Thank you @Gabri95 for the prompt reply and the explanation. I will close the issue.

from e2cnn.