[D] Design a network what combines supervised (CNN) and unsupervised (AE) for classification task
Hello everyone! Working under one interesting problem, as you can read from post name, and wonder does anyone have ideas or hints for it? As we know autoencoders take input (in my case it’s an image from the popular dataset) and reconstruct it as an output. Let’s call input – node 1, output – node 3. It creates valuable features at its hidden layers (let’s call it node 2) during the process. Let’s hypothesize, that if node 2 is used as input for CNN then the classification will be improved. My current ideas are:
1 – For now, it sounds interesting and reasonable to try use output of the encoder – latent space representation as an input for following CNN.
2 – Use one of the decoder layers as input for CNN.
A possible purpose of it – try to get more important futures from class imbalanced data. (As an example – from 5 classes 1 of them contain 50% fewer images than other). Let’s discuss?