  Established experimental procedures for one-shot machine learning do not test
the ability to learn or remember specific instances of classes, a key feature
of animal intelligence. Distinguishing specific instances is necessary for many
real-world tasks, such as remembering which cup belongs to you. Generalisation
within classes conflicts with the ability to separate instances of classes,
making it difficult to achieve both capabilities within a single architecture.
We propose an extension to the standard Omniglot classification-generalisation
framework that additionally tests the ability to distinguish specific instances
after one exposure and introduces noise and occlusion corruption. Learning is
defined as an ability to classify as well as recall training samples.
Complementary Learning Systems (CLS) is a popular model of mammalian brain
regions believed to play a crucial role in learning from a single exposure to a
stimulus. We created an artificial neural network implementation of CLS and
applied it to the extended Omniglot benchmark. Our unsupervised model
demonstrates comparable performance to existing supervised ANNs on the Omniglot
classification task (requiring generalisation), without the need for
domain-specific inductive biases. On the extended Omniglot instance-recognition
task, the same model also demonstrates significantly better performance than a
baseline nearest-neighbour approach, given partial occlusion and noise.
