The overtone model of self-deception

In this paper I will argue for what I call an ‘overtone model of self-deception’. The analogy to overtones (higher-order frequencies of a tone) is as follows: a self-deceiver’s optimal degree of instability (the term is borrowed from Friston et al. 2012, and applied to self-deception) is elevated so that constant exploration (of a certain number of hypotheses) is pursued instead of disambiguation in favor of a certain hypothesis. These hypotheses are explored in parallel (for similar ideas with respect to higher-order cognition in general see Pezzulo and Cisek 2016, and Metzinger 2017) and are like overtones of the currently active self-deceptive hypothesis (the base frequency) so that what we as self-deceivers, as well as observers, perceive as one tone (self-deception) is actually a fusion of different frequencies. The term ‘fusion’ is relevant because the phenomenology of the self-deceiver is co-determined by overtones

A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning

Current deep learning research is dominated by benchmark evaluation. A method is regarded as favorable if it empirically performs well on the dedicated test set. This mentality is seamlessly reflected in the resurfacing area of continual learning, where consecutively arriving sets of benchmark data are investigated. The core challenge is framed as protecting previously acquired representations from being catastrophically forgotten due to the iterative parameter updates. However, comparison of individual methods is nevertheless treated in isolation from real world application and typically judged by monitoring accumulated test set performance. The closed world assumption remains predominant. It is assumed that during deployment a model is guaranteed to encounter data that stems from the same distribution as used for training. This poses a massive challenge as neural networks are well known to provide overconfident false predictions on unknown instances and break down in the face of corrupted data. In this work we argue that notable lessons from open set recognition, the identification of statistically deviating data outside of the observed dataset, and the adjacent field of active learning, where data is incrementally queried such that the expected performance gain is maximized, are frequently overlooked in the deep learning era. Based on these forgotten lessons, we propose a consolidated view to bridge continual learning, active learning and open set recognition in deep neural networks. Our results show that this not only benefits each individual paradigm, but highlights the natural synergies in a common framework. We empirically demonstrate improvements when alleviating catastrophic forgetting, querying data in active learning, selecting task orders, while exhibiting robust open world application where previously proposed methods fail.Comment: 32 page