Learning from One and Only One Shot
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Humans can generalize from only a few examples and from little pre-training on similar tasks. Yet, machine learning (ML) typically requires large data to learn or pre-learn to transfer. Inspired by nativism, we directly model basic human-innate priors in abstract visual tasks e.g., character/doodle recognition. This yields a white-box model that learns general-appearance similarity – how any two images look in general – by mimicking how humans naturally "distort" an object at first sight. Using simply the nearest-neighbor classifier on this similarity space, we achieve human-level character recognition using only 1–10 examples per class and nothing else (no pre-training). This differs from few-shot learning (FSL) using significant pre-training. On standard benchmarks MNIST/EMNIST and the Omniglot challenge, we outperform both neural-network-based and classical ML in the "tiny-data" regime, including FSL pre-trained on large data. Our model enables unsupervised learning too: by learning the non-Euclidean, general-appearance similarity space in a k-means style, we can generate human-intuitive archetypes as cluster “centroids”.
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