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Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks

2021-01-07 18:41:16
Sebastian Palacio, Philipp Engler, Jörn Hees, Andreas Dengel

Abstract

tract: 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.

Abstract (translated)

URL

https://arxiv.org/abs/2101.03057

PDF

https://arxiv.org/pdf/2101.03057


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