Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition [or: How the brain could maintain stability in a Heraclitean universe.]

That's (the stuff outside the brackets) is the title of a most interesting paper:

Mundt, M.; Pliushch, I.; Majumder, S.; Hong, Y.; Ramesh, V. Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition. J. Imaging 2022,8,93. https://doi.org/10.3390/jimaging8040093

Abstract: Modern deep neural networks are well known to be brittle in the face of unknown data instances and recognition of the latter remains a challenge. Although it is inevitable for continual-learning systems to encounter such unseen concepts, the corresponding literature appears to nonetheless focus primarily on alleviating catastrophic interference with learned representations. In this work, we introduce a probabilistic approach that connects these perspectives based on variational inference in a single deep autoencoder model. Specifically, we propose to bound the approximate posterior by fitting regions of high density on the basis of correctly classified data points. These bounds are shown to serve a dual purpose: unseen unknown out-of-distribution data can be distinguished from already trained known tasks towards robust application. Simultaneously, to retain already acquired knowledge, a generative replay process can be narrowed to strictly in-distribution samples, in order to significantly alleviate catastrophic interference.

From the paper:

In particular, should we wish to apply and extend the system to an open world, where several other animals (and non animals) exist, there are two critical questions: (a) How can we prevent obvious mispredictions if the system encounters a new class? (b) How can we continue to incorporate this new concept into our present system without full retraining? With respect to the former question, it is well known that neural networks yield overconfident mispredictions in the face of unseen unknown concepts [3], a realization that has recently resurfaced in the context of various deep neural networks [4–6]. With respect to the latter question, it is similarly well known that neural networks, which are trained exclusively on newly arriving data, will overwrite their representations and thus forget encoded knowledge—a phenomenon referred to as catastrophic interference or catastrophic forgetting [7,8]. Although we have worded the above questions in a way that naturally exposes their connection: to identify what is new and think about how new concepts can be incorporated, they are largely subject to separate treatment in the respective literature. While open-set recognition [1,9,10] aims to explicitly identify novel inputs that deviate with respect to already observed instances, the existing continual learning literature predominantly concentrates its efforts on finding mechanisms to alleviate catastrophic interference (see [11] for an algorithmic survey).

Compare with this post from last year: Consciousness, reorganization and polyviscosity, Part 1: The link to Powers.