Learning from One and Only One Shot

• URL: http://arxiv.org/abs/2201.08815v2
• Date: Tue, 21 May 2024 05:19:52 GMT
• Title: Learning from One and Only One Shot
• Authors: Haizi Yu, Igor Mineyev, Lav R. Varshney, James A. Evans,
• Abstract summary: Humans can generalize from only a few examples and from little pretraining on similar tasks. Motivated by nativism and artificial general intelligence, we model human-innate priors in abstract visual tasks. We achieve human-level recognition with only $1$--$10$ examples per class and no pretraining.
• Score: 15.835306446986856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans can generalize from only a few examples and from little pretraining on similar tasks. Yet, machine learning (ML) typically requires large data to learn or pre-learn to transfer. Motivated by nativism and artificial general intelligence, we directly model human-innate priors in abstract visual tasks such as character and doodle recognition. This yields a white-box model that learns general-appearance similarity by mimicking how humans naturally ``distort'' an object at first sight. Using just nearest-neighbor classification on this cognitively-inspired similarity space, we achieve human-level recognition with only $1$--$10$ examples per class and no pretraining. This differs from few-shot learning that uses massive pretraining. In the tiny-data regime of MNIST, EMNIST, Omniglot, and QuickDraw benchmarks, we outperform both modern neural networks and classical ML. For unsupervised learning, by learning the non-Euclidean, general-appearance similarity space in a $k$-means style, we achieve multifarious visual realizations of abstract concepts by generating human-intuitive archetypes as cluster centroids.

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