Ignoring one/multiple channels in pytorch-model during training and evaluation
The quick-and-dirty solution would be to simply add a pre-processing stage where you multiply this un-wanted channel by zero.
For my ML-project I have 1d-data with multiple channels (n>2, variable). After data acquisition I noticed that the data in one channel was completely unusable, and therefore would decrease the accuracy of my trained model. Still, I did not want to remove the channel entirely from my model and re-write it into a model with (n-1)-channels, as it would receive future data with n channels during classification, which would break a modified model.
Instead, I wanted to have the option of telling my model to ignore data coming from one channel during both training and evaluation, such that it would look and behave like a model with n channels, but would internally only use n-1 channels. Is that possible for pytorch-based neural networks? And if yes, how would I approach that?