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Production deployments in complex systems require ML architectures to behighly efficient and usable against multiple tasks. Particularly demanding areclassification problems in which data arrives in a streaming fashion and eachclass is presented separately. Recent methods with stochastic gradient learninghave been shown to struggle in such setups or have limitations like memorybuffers, and being restricted to specific domains that disable its usage inreal-world scenarios. For this reason, we present a fully differentiablearchitecture based on the Mixture of Experts model, that enables the trainingof high-performance classifiers when examples from each class are presentedseparately. We conducted exhaustive experiments that proved its applicabilityin various domains and ability to learn online in production environments. Theproposed technique achieves SOTA results without a memory buffer and clearlyoutperforms the reference methods.

Domain-Agnostic Neural Architecture for Class Incremental Continual Learning in Document Processing Platform