The learned shape descriptor is invariant to affine transformations, including shifts, rotations and scaling. Thanks to the adopted autoencoder framework, inter-subject differences are automatically ...

Using baseband signals and steering angles as inputs, angle-inclusive DPD utilizes a 2D Convolutional Neural Network (2D-CNN) to pre-distort signals, achieving continuous linearization across the beam ...

This is an autoencoder with cylic loss and coding parsing loss for image compression and reconstruction. Network backbone is simple 3-layer fully conv (encoder) and symmetrical for decoder. Finally it ...