  A feature learning task involves training models that are capable of
inferring good representations (transformations of the original space) from
input data alone. When working with limited or unlabelled data, and also when
multiple visual domains are considered, methods that rely on large annotated
datasets, such as Convolutional Neural Networks (CNNs), cannot be employed. In
this paper we investigate different auto-encoder (AE) architectures, which
require no labels, and explore training strategies to learn representations
from images. The models are evaluated considering both the reconstruction error
of the images and the feature spaces in terms of their discriminative power. We
study the role of dense and convolutional layers on the results, as well as the
depth and capacity of the networks, since those are shown to affect both the
dimensionality reduction and the capability of generalising for different
visual domains. Classification results with AE features were as discriminative
as pre-trained CNN features. Our findings can be used as guidelines for the
design of unsupervised representation learning methods within and across
domains.
