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''.

该研究运用天赋先验直接模拟人类在抽象视觉任务（例如字符 / 涂鸦识别）中的基本天赋先验，使得机器学习可以在类似于人类只使用很少的样例和预训练的情况下进行泛化，并保持和人类人类识别率相同。