  Classification problems solved with deep neural networks (DNNs) typically
rely on a closed world paradigm, and optimize over a single objective (e.g.,
minimization of the cross-entropy loss). This setup dismisses all kinds of
supporting signals that can be used to reinforce the existence or absence of a
particular pattern. The increasing need for models that are interpretable by
design makes the inclusion of said contextual signals a crucial necessity. To
this end, we introduce the notion of Self-Supervised Autogenous Learning (SSAL)
models. A SSAL objective is realized through one or more additional targets
that are derived from the original supervised classification task, following
architectural principles found in multi-task learning. SSAL branches impose
low-level priors into the optimization process (e.g., grouping). The ability of
using SSAL branches during inference, allow models to converge faster, focusing
on a richer set of class-relevant features. We show that SSAL models
consistently outperform the state-of-the-art while also providing structured
predictions that are more interpretable.
