Secondly, our problem setting is different from the former studies, which usually deal with classification tasks and fine-tune the deep neural networks using training data with explicit class labels; in contrast we fine-tune our autoencoders using a semi-supervised criterion.
we use indirect supervision in the form of object classification in addition to the objective of reconstructing the attribute-centric input representation.