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Unsupervised One-Shot Learning of Both Specific Instances and Generalised Classes with a Hippocampal Architecture

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AI 2020: Advances in Artificial Intelligence (AI 2020)

Abstract

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.

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Notes

  1. 1.

    https://github.com/Cerenaut/aha.

  2. 2.

    Adam Optimizer is reset and trainable parameters are re-initialised.

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Acknowledgment

Thanks to Elkhonon Goldberg for enriching discussions on the hippocampal region and to Rotem Aharon for insights and analysis of Hopfield networks.

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Correspondence to Gideon Kowadlo .

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Kowadlo, G., Ahmed, A., Rawlinson, D. (2020). Unsupervised One-Shot Learning of Both Specific Instances and Generalised Classes with a Hippocampal Architecture. In: Gallagher, M., Moustafa, N., Lakshika, E. (eds) AI 2020: Advances in Artificial Intelligence. AI 2020. Lecture Notes in Computer Science(), vol 12576. Springer, Cham. https://doi.org/10.1007/978-3-030-64984-5_31

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  • DOI: https://doi.org/10.1007/978-3-030-64984-5_31

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  • Online ISBN: 978-3-030-64984-5

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