text: 147,21,1134402279656828928,872072000906424321,Martin Mundt,"Maybe my favorite figure from our new continual learning paper: <LINK>  I find it cool how our approach can decide if samples from the prior will generate ambiguous or class interpolated images that lead to confusion, before even decoding. Helps quite a bit. <LINK>",https://arxiv.org/abs/1905.12019,"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. ","Unified Probabilistic Deep Continual Learning through Generative Replay