In this case of Deep linear autoencoder, when we use six hidden layers with the following linear structure inside the encoder: 28 * 28 -> 512 -> 256 -> 128 -> 64 -> 16 -> 3, the performance of the ...

In this paper, a feature learning method using a stacked contractive autoencoder (sCAE) is presented to extract the temporal change feature from superpixel with noise suppression. First, an affiliated ...

This paper proposes a novel deep learning approach to address these challenges, utilizing a Contractive Autoencoder (CAE) model optimized and applied specifically to noisy temporal heart rate data ...