Swapping Autoencoder consists of autoencoding (top) and swapping (bottom) operation.Top: An encoder E embeds an input (Notre-Dame) into two codes.The structure code is a tensor with spatial dimensions ...

In this assignment, we explore how to learn meaningful representations from unlabelled image data, which can then be applied to classify images with minimal labelled examples. Our main focus is on ...

Three autoencoder are used for this research work Simple autoencoder, Deep autoencoder and Convolutional autoencoder. To reduced search indexing time, for extracting similar images from the dataset we ...

To ensure a fair comparison with Mostafa’s proposed CAE model, we maintained consistency in data processing, using T1-weighted MRI slice images of the healthy control group for autoencoder ...

Due to the complexity of samples and the limitations in spatial resolution, the spectra in hyperspectral imaging (HSI) are generally contributed to by multiple components, making univariate analysis ...

At 1 sample per pixel (spp), the Monte Carlo integration of indirect illumination results in very noisy images, and the problem can therefore be framed as reconstruction instead of denoising. Previous ...

Keywords: hyperspectral images, spectral unmixing, endmembers, abundance maps, image processing, deep learning, autoencoder, algal bloom. Citation: Alfaro-Mejía E, Manian V, Ortiz JD and Tokars RP ...