Abstract: In this innovative study, a Vision Transformer and Residual Network-based Autoencoder is employed for the efficient encoding of RGBD data, aimed at enhancing robotic precision in grasping ...

a signal-guided masked autoencoder (S-MAE) based semi-supervised learning framework is proposed for high-precision positioning with limited labeled channel impulse response (CIR) samples. To release ...

Most of my effort was spent on training denoise autoencoder networks to capture the relationships among inputs and use the learned representation for downstream supervised models. The network is an ...

The primary objective of this project is to develop an efficient model for data compression. The focus is on leveraging complex autoencoder architectures to achieve significant dimensionality ...