  Supervised deep neural networks are known to undergo a sharp decline in the
accuracy of older tasks when new tasks are learned, termed "catastrophic
forgetting". Many state-of-the-art solutions to continual learning rely on
biasing and/or partitioning a model to accommodate successive tasks
incrementally. However, these methods largely depend on the availability of a
task-oracle to confer task identities to each test sample, without which the
models are entirely unable to perform. To address this shortcoming, we propose
and compare several candidate task-assigning mappers which require very little
memory overhead: (1) Incremental unsupervised prototype assignment using either
nearest means, Gaussian Mixture Models or fuzzy ART backbones; (2) Supervised
incremental prototype assignment with fast fuzzy ARTMAP; (3) Shallow perceptron
trained via a dynamic coreset. Our proposed model variants are trained either
from pre-trained feature extractors or task-dependent feature embeddings of the
main classifier network. We apply these pipeline variants to continual learning
benchmarks, comprised of either sequences of several datasets or within one
single dataset. Overall, these methods, despite their simplicity and
compactness, perform very close to a ground truth oracle, especially in
experiments of inter-dataset task assignment. Moreover, best-performing
variants only impose an average cost of 1.7% parameter memory increase.
