Regarding the Autoencoder model about deepsphere-cosmo-tf2 HOT 4 OPEN

Hello,

Thanks for your interest in deepsphere.

What noise levels (SNR) are you talking about? The standard graph layers in Deepsphere can struggle with high frquencies, so it might be good to interlace some Conv1D layers or go with a UNet + Skips architecture.

I might be able to brew together an example later this week.

Best,

Janis

from deepsphere-cosmo-tf2.

Hi Janis,

Thanks for your reply. So, due to the nature of my study, a 'true label' is not generally available. For training purpose, I tried with a set of training data with SNR 25, and created a 'true lable data' by amplifying signal information in the training data to SNR 45. I guess my exception was, that the model might be able to recover the loud features better and reduce/remove the rest while decoding. Thanks for the suggestion with UNet++ skip connection. I shall look into it. I hate to burden you like this in your busy schedule, but an example will be a tremendous help.

Many Thanks
Wasim

from deepsphere-cosmo-tf2.

Dear Wasim

Sorry for the delay, I was a bit busier than expected.

I just pushed a new branch that contains a new example notebook with a denoising UNet (see commit 86d5e6d). I tested it with the same toy data and it seems to do what I'd expect.

As I mentioned earlier, it might be good to add some Conv1D layers to the blocks to increase the capacity of the model. So I'd recommend to use this example as a template and play around until you have an architecture that works for you.

Best,
Janis

from deepsphere-cosmo-tf2.

Hi Janis,

No no, I am really grateful for your help and that you made time for me during holidays. I am running this model now and I can see it is in the right direction for my expected outcome. I would try adjusting it with my requirements. Thank you so much for the tutorial.

Many Thanks
Wasim

from deepsphere-cosmo-tf2.