Neuro-mimetic Task-free Unsupervised Online Learning with Continual
  Self-Organizing Maps
Hitesh Vaidya Travis Desell Ankur Mali Alexander Ororbia
An intelligent system capable of continual learning is one that can process
and extract knowledge from potentially infinitely long streams of pattern
vectors. The major challenge that makes crafting such a system difficult is
known as catastrophic forgetting - an agent, such as one based on artificial
neural networks (ANNs), struggles to retain previously acquired knowledge when
learning from new samples. Furthermore, ensuring that knowledge is preserved
for previous tasks becomes more challenging when input is not supplemented with
task boundary information. Although forgetting in the context of ANNs has been
studied extensively, there still exists far less work investigating it in terms
of unsupervised architectures such as the venerable self-organizing map (SOM),
a neural model often used in clustering and dimensionality reduction. While the
internal mechanisms of SOMs could, in principle, yield sparse representations
that improve memory retention, we observe that, when a fixed-size SOM processes
continuous data streams, it experiences concept drift. In light of this, we
propose a generalization of the SOM, the continual SOM (CSOM), which is capable
of online unsupervised learning under a low memory budget. Our results, on
benchmarks including MNIST, Kuzushiji-MNIST, and Fashion-MNIST, show almost a
two times increase in accuracy, and CIFAR-10 demonstrates a state-of-the-art
result when tested on (online) unsupervised class incremental learning setting.