On autoencoder scoring


Hanna Kamyshanska, Roland Memisevic ;
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(3):720-728, 2013.


Autoencoders are popular feature learning models because they are conceptually simple, easy to train and allow for efficient inference and training. Recent work has shown how certain autoencoders can assign an unnormalized “score” to data which measures how well the autoencoder can represent the data. Scores are commonly computed by using training criteria that relate the autoencoder to a probabilistic model, such as the Restricted Boltzmann Machine. In this paper we show how an autoencoder can assign meaningful scores to data independently of training procedure and without reference to any probabilistic model, by interpreting it as a dynamical system. We discuss how, and under which conditions, running the dynamical system can be viewed as performing gradient descent in an energy function, which in turn allows us to derive a score via integration. We also show how one can combine multiple, unnormalized scores into a generative classifier.

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