Abstract
Existing deep neural networks mainly focus on
learning transformation invariant features. However, it is the equivariant features that are more adequate for general purpose tasks. Unfortunately, few
work has been devoted to learning equivariant features. To fill this gap, in this paper, we propose
an affine equivariant autoencoder to learn features
that are equivariant to the affine transformation in
an unsupervised manner. The objective consists
of the self-reconstruction of the original example
and affine transformed example, and the approximation of the affine transformation function, where
the reconstruction makes the encoder a valid feature extractor and the approximation encourages
the equivariance. Extensive experiments are conducted to validate the equivariance and discriminative ability of the features learned by our affine
equivariant autoencoder