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Application of Quantum Pre-Processing Filter for Binary Image Classification with Small Samples
Similar to our previous multi-class classification results, the application of QPF improved the binary image classification accuracy using neural network against MNIST, EMNIST, and CIFAR-10 from 98. 9% to 99. 2%, 97. 8% to 98. 3%, and 71. 2% to 76. 1%, respectively, but degraded it against GTSRB from 93. 5% to 92. 0%.
Development of a Novel Quantum Pre-processing Filter to Improve Image Classification Accuracy of Neural Network Models
This paper proposes a novel quantum pre-processing filter (QPF) to improve the image classification accuracy of neural network (NN) models.
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