Second, the applied deep learning method is based on an autoencoder where a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is utilized as the autoencoder ...

A tailored autoencoder architecture is then trained to reconstruct MRI slices while learning the spatial characteristics of MRI slices at fiducial points across axial, coronal, and sagittal planes.

Figure 1. Basic Autoencoder architecture, showing encoder and decoder components [22]. Figure 2. AE-based framework for signal reconstruction, highlighting latent space compression. Recent ...

The project presents a Deep Learning model with an autoencoder-like architecture making use of convolutional layers in both the encoder and the decoder to perform image inpainting over the CIFAR-10 ...

PyTorch implementation of Masked AutoEncoder Due to limited resources, I only test my randomly designed ViT-Tiny on the CIFAR10 dataset. It is not my goal to reproduce MAE perfectly, but my ...

As an alternative, we trained modified autoencoder networks to mimic human-like behavior in a binaural detection task. The autoencoder architecture emphasizes interpretability and, hence, we “opened ...

CIFAR-10 problems analyze crude 32 x 32 color images to predict which of 10 classes the image is. Here, Dr. James McCaffrey of Microsoft Research shows how to create a PyTorch image classification ...