Abstract
Unlike most computer vision approaches, which depend on hundreds or thousands of training images, humans can typically learn from a single visual example. Humans achieve this ability using background knowledge. Rule-based machine learning approaches such as Inductive Logic Programming (ILP) provide a framework for incorporating domain specific background knowledge. These approaches have the potential for human-like learning from small data or even one-shot learning, i.e. learning from a single positive example. By contrast, statistics based computer vision algorithms, including Deep Learning, have no general mechanisms for incorporating background knowledge. This paper presents an approach for one-shot rule learning called One-Shot Hypothesis Derivation (OSHD) based on using a logic program declarative bias. We apply this approach to two challenging human-like computer vision tasks: 1) Malayalam character recognition and 2) neurological diagnosis using retinal images. We compare our results with a state-of-the-art Deep Learning approach, called Siamese Network, developed for one-shot learning. The results suggest that our approach can generate human-understandable rules and outperforms the deep learning approach with a significantly higher average predictive accuracy.
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Acknowledgements
Dany Varghese was supported by Vice Chancellor’s PhD Scholarship Award at the University of Surrey. Roman Bauer was supported by the Engineering and Physical Sciences Research Council of the United Kingdom (EP/S001433/1). The authors also thankfully acknowledge that the retinal images were obtained using UK Biobank application number 1969. Alireza Tamaddoni-Nezhad and Stephen Muggleton were supported by the EPSRC Network Plus grant on Human-Like Computing (HLC).
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Varghese, D., Bauer, R., Baxter-Beard, D., Muggleton, S., Tamaddoni-Nezhad, A. (2022). Human-Like Rule Learning from Images Using One-Shot Hypothesis Derivation. In: Katzouris, N., Artikis, A. (eds) Inductive Logic Programming. ILP 2021. Lecture Notes in Computer Science(), vol 13191. Springer, Cham. https://doi.org/10.1007/978-3-030-97454-1_17
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