Superposition as Data Augmentation using LSTM and HMM in Small Training Sets
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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 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 1.1 900 samples in MNIST using 3-layer stacked LSTM.
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