We introduce a cluster-based generative image segmentation framework to encode higher-level representations of visual concepts based on one-shot learning inspired by the Omniglot Challenge. The inferred parameters of each component of a Gaussian Mixture Model (GMM) represent a distinct topological subpart of a visual concept. Sampling new data from these parameters generates augmented subparts to build a more robust prototype for each concept, i.e., the Abstracted Gaussian Prototype (AGP). This framework addresses one-shot classification tasks using a cognitively-inspired similarity metric and addresses one-shot generative tasks through a novel AGP-VAE pipeline employing variational autoencoders (VAEs) to generate new class variants. Results from human judges reveal that the generative pipeline produces novel examples and classes of visual concepts that are broadly indistinguishable from those made by humans. The proposed framework leads to impressive, but not state-of-the-art, classification accuracy; thus, the contribution is two-fold: 1) the system is low in theoretical and computational complexity yet achieves the standard of 'true' one-shot learning by operating in a fully standalone manner unlike existing approaches that draw heavily on pre-training or knowledge engineering; and 2) in contrast with existing neural network approaches, the AGP approach addresses the importance of broad task capability emphasized in the Omniglot challenge (successful performance on classification and generative tasks). These two points are critical in advancing our understanding of how learning and reasoning systems can produce viable, robust, and flexible concepts based on literally no more than a single example.

翻译：我们提出了一种基于聚类的生成式图像分割框架，用于基于单样本学习编码视觉概念的更高层次表示，其灵感来源于Omniglot挑战赛。高斯混合模型（GMM）各分量的推断参数代表了视觉概念的一个独特拓扑子部分。从这些参数中采样新数据可以生成增强的子部分，从而为每个概念构建更鲁棒的原型，即抽象高斯原型（AGP）。该框架使用一种受认知启发的相似性度量来处理单样本分类任务，并通过一种新颖的AGP-VAE流程（利用变分自编码器（VAE）生成新的类别变体）来处理单样本生成任务。人类评估者的结果显示，该生成流程产生的视觉概念新样本和新类别在总体上与人类创造的样本难以区分。所提出的框架实现了令人印象深刻（尽管并非最先进）的分类准确率；因此，其贡献是双重的：1）该系统理论和计算复杂度低，却通过完全独立运行的方式达到了“真实”单样本学习的标准，这与严重依赖预训练或知识工程的现有方法不同；2）与现有的神经网络方法相比，AGP方法解决了Omniglot挑战赛中强调的广泛任务能力的重要性（在分类和生成任务上均能成功表现）。这两点对于推进我们理解学习和推理系统如何仅基于单个样本就能产生可行、鲁棒且灵活的概念至关重要。