I plan to use autoencoder for feature extraction, then use the latent vector for clustering.
My autoencoder performs very very well on my training set (loss small and reconstructed image look very similar to input) but shows lower performance on validation set.
Is it a good idea to NOT use validation set but only a training set? Since I can reach good performance extracted vector should be "good" features
If I use new data I'll need to retrain the model, but I should always be able to reach good performances since new data will be very similar to current data