I am new to deep learning.
I have a dataset of images of varying dimensions of a certain object. A few images of the object are also in varying orientations. The objective is to learn the features of the object (using Autoencoders).
Is it possible to create a network with layers that account for varying dimensions and orientations of the input image, or should I strictly consider a dataset containing images of uniform dimensions? What is the necessary criteria of an eligible dataset to be used for training a Deep Network in general.
The idea is, I want to avoid pre-processing my dataset by normalizing it via scaling, re-orienting operations etc. I would like my network to account for the variability in dimensions and orientations. Please point me to resources for the same.