One-shot Learning with Memory-Augmented Neural Networks

TL;DR

This paper introduces a memory-augmented neural network capable of one-shot learning, efficiently encoding new information and making accurate predictions with minimal samples, advancing the ability of neural networks to learn from very limited data.

Contribution

The paper presents a novel memory-augmented neural network architecture and a new memory access method that emphasizes content-based retrieval for improved one-shot learning.

Findings

Demonstrates rapid assimilation of new data with high accuracy

Shows superiority over traditional models in one-shot learning tasks

Introduces a content-focused memory access mechanism

Abstract

Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of "one-shot learning." Traditional gradient-based networks require a lot of data to learn, often through extensive iterative training. When new data is encountered, the models must inefficiently relearn their parameters to adequately incorporate the new information without catastrophic interference. Architectures with augmented memory capacities, such as Neural Turing Machines (NTMs), offer the ability to quickly encode and retrieve new information, and hence can potentially obviate the downsides of conventional models. Here, we demonstrate the ability of a memory-augmented neural network to rapidly assimilate new data, and leverage this data to make accurate predictions after only a few samples. We also introduce a new method for accessing an external memory that focuses on memory content, unlike previous methods that additionally use memory location-based focusing mechanisms.