Superposition as Data Augmentation using LSTM and HMM in Small Training Sets

TL;DR

This paper introduces a quantum-inspired superposition data augmentation technique for small training sets, improving accuracy in audio and image recognition tasks over traditional methods.

Contribution

It presents a novel augmentation method based on quantum superposition principles, outperforming mix-up augmentation in small data scenarios.

Findings

3% accuracy improvement in Russian audio-digits recognition with fewer samples

7.16% better accuracy than mix-up with 500 samples using HMM

1.1% accuracy gain over mix-up on MNIST with LSTM

Abstract

Considering audio and image data as having quantum nature (data are represented by density matrices), we achieved better results on training architectures such as 3-layer stacked LSTM and HMM by mixing training samples using superposition augmentation and compared with plain default training and mix-up augmentation. This augmentation technique originates from the mix-up approach but provides more solid theoretical reasoning based on quantum properties. We achieved 3% improvement (from 68% to 71%) by using 38% lesser number of training samples in Russian audio-digits recognition task and 7,16% better accuracy than mix-up augmentation by training only 500 samples using HMM on the same task. Also, we achieved 1.1% better accuracy than mix-up on first 900 samples in MNIST using 3-layer stacked LSTM.