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

24 Oct 2019Akilesh SivaswamyEvgeny Pavlovskiy

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... (read more)

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