Part-based approximations for morphological operators using asymmetric auto-encoders
This work addresses the need for interpretable, online part-based decomposition in image datasets, though it is incremental as it builds on existing auto-encoder and sparse coding techniques.
The paper tackled the problem of building part-based, interpretable image representations by developing a sparse, non-negative auto-encoder with an asymmetric architecture. It achieved favorable results compared to state-of-the-art online methods on MNIST and Fashion MNIST datasets, as measured by classical metrics and a new invariance-based metric.
This paper addresses the issue of building a part-based representation of a dataset of images. More precisely, we look for a non-negative, sparse decomposition of the images on a reduced set of atoms, in order to unveil a morphological and interpretable structure of the data. Additionally, we want this decomposition to be computed online for any new sample that is not part of the initial dataset. Therefore, our solution relies on a sparse, non-negative auto-encoder where the encoder is deep (for accuracy) and the decoder shallow (for interpretability). This method compares favorably to the state-of-the-art online methods on two datasets (MNIST and Fashion MNIST), according to classical metrics and to a new one we introduce, based on the invariance of the representation to morphological dilation.