The autoencoder architecture consists of two parts: encoder and decoder. Each part consists of 3 Linear layers with ReLU activations. The last activation layer is Sigmoid. The training was done for ...

Exploring advanced autoencoder architectures for efficient data compression on EMNIST dataset, focusing on high-fidelity image reconstruction with minimal information loss. This project tests various ...

2.2. Neural Network Architecture: Encoder. A variational autoencoder (Kingma and Welling, 2013; Doersch, 2016) consists of an encoder and a decoder. We propose the following architecture for them. The ...