@ptrblck make sense , but still i need to understand it more deeper , i may ask you more detail please bear me with more silly question , i implemented the same on my data , there i see a little bit improvement.
The improvement i am measuring by feeding the latent/bottleneck into a boosted algo LGBM for classification , i mean after autoencoder training i use the same autoencoder model to get the latent space of my training data and then use this latent space to train classification using LGBM. Before using this above technique as mentioned above two heads , LGBM model accuracy was 0.58 which increase to after using it to 0.59
I noticed there are data issue as well , data are too fuzzy you cant discriminate the two records which more or less are same but one has target value 1 and other is 0 , all features has the same problem and this problem is widespread in all rows , very very overlapped data
I am thinking create different auto-encoder with different architecture and averaged all their latent space to get final latent space and feed into LGBM to see any improvement, Is their any systematic way to achieve the same in pytorch?
Please let me know how can i approach to solve this problem if you have any idea please share