Abstracted Gaussian Prototypes for True One-Shot Concept Learning

TL;DR

This paper presents a novel cluster-based generative framework called Abstracted Gaussian Prototype (AGP) for true one-shot concept learning, capable of both classification and generation without heavy pre-training.

Contribution

The introduction of AGP as a low-complexity, standalone system that achieves true one-shot learning and broad task capability, unlike existing methods.

Findings

Generates human-indistinguishable novel visual concepts.

Achieves standard one-shot classification accuracy.

Addresses both classification and generative tasks effectively.

Abstract

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.