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.

翻译：人类能够仅凭少数示例并在无需大量相似任务预训练的情况下进行泛化。然而，机器学习通常需要大规模数据才能学习或通过预训练实现迁移。受先天论与通用人工智能启发，我们直接对字符识别、涂鸦识别等抽象视觉任务中人类固有的先验知识进行建模，构建了一个白盒模型。该模型通过模拟人类初次观察物体时自然的“扭曲”过程，学习通用外观相似性。仅需基于这一认知启发的相似性空间进行最近邻分类，我们即在每类仅需1-10个样本且无需预训练的条件下达到了人类水平的识别能力，这有别于依赖大规模预训练的少样本学习。在MNIST、EMNIST、Omniglot和QuickDraw基准测试的小数据场景中，我们的方法优于现代神经网络与经典机器学习方法。在无监督学习方面，通过采用类k-means方式学习非欧几里得空间的通用外观相似性，我们生成了具有人类直觉的原型作为聚类质心，从而实现了抽象概念的多形态视觉具象化。