Augmented Memory Networks for Streaming-Based Active One-Shot Learning

TL;DR

This paper introduces a memory-augmented reinforcement learning approach with Class Margin Sampling for active one-shot learning in streaming data, significantly improving label efficiency and prediction accuracy.

Contribution

It proposes integrating memory-augmented neural networks and a novel sampling strategy into reinforcement learning for active one-shot learning.

Findings

Outperforms existing baselines in accuracy and label request reduction

Enhances sample efficiency in streaming classification tasks

Demonstrates effectiveness of memory and sampling strategies in RL-based AL

Abstract

One of the major challenges in training deep architectures for predictive tasks is the scarcity and cost of labeled training data. Active Learning (AL) is one way of addressing this challenge. In stream-based AL, observations are continuously made available to the learner that have to decide whether to request a label or to make a prediction. The goal is to reduce the request rate while at the same time maximize prediction performance. In previous research, reinforcement learning has been used for learning the AL request/prediction strategy. In our work, we propose to equip a reinforcement learning process with memory augmented neural networks, to enhance the one-shot capabilities. Moreover, we introduce Class Margin Sampling (CMS) as an extension of the standard margin sampling to the reinforcement learning setting. This strategy aims to reduce training time and improve sample efficiency in the training process. We evaluate the proposed method on a classification task using empirical accuracy of label predictions and percentage of label requests. The results indicates that the proposed method, by making use of the memory augmented networks and CMS in the training process, outperforms existing baselines.