Image-to-sketch-using-autoencoder. An autoencoder is a unique form of deep learning architecture that comprises two main components: an encoder and a decoder. The encoder may take the form of either a ...

In this project, I have built an image retrieval system using the AutoEncoder neural network. The network was trained with the CIFAR-10 dataset. The hyperparameters for training the model are provided ...

For instance, you could train an autoencoder on grainy images and then use the trained model to remove the grain/noise from the image. Autoencoder Architecture. Let’s take a look at the architecture ...

The work is devoted to the process of investigation and development of the approach to the reconstruction of images taken from unmanned aerial vehicles (drones) for further work with them. Proposal of ...

The inherent complexity of image colorization has motivated computer scientists towards the development of algorithms capable of simplifying the image colorization process. Despite the numerous ...

2.1 Autoencoder model architecture 2.1.1 Model architecture. The autoencoder is composed of an encoder and a decoder. Figure 1 displays the structure of this one-dimensional autoencoder, which ...

This increased depth reduces the computational cost of representing some functions and it decreases the amount of training data required to learn some functions. The popular applications of ...

Researchers from Rutgers University propose a slot-based autoencoder architecture called SLot Attention TransformEr (SLATE). The SLATE model is a combination of the best from DALL·E and object-centric ...