The experimental results show that this method can effectively integrate the channel attention module and the fully convolutional autoencoder. Although it is an unsupervised feature learning model, it ...

In order to conquer this problem, a lightweight framework named “PDSE-Lite” based on Convolutional Autoencoder (CAE ... the output features maps of pretrained bottleneck, Conv2D #5, and Conv2D #6 ...

In this article, we are going to see how we can remove noise from the image data using an encoder-decoder model. Having clear and processed images or videos is very important in any computer vision ...

In our last article, we demonstrated the implementation of Deep Autoencoder in image reconstruction. In this article, we will define a Convolutional Autoencoder in PyTorch and train it on the CIFAR-10 ...

While we all understand the usefulness of 'normal' convolutional layers ... It is then followed by three Conv2D layers, forming the 'encoder' part of our autoencoder. The Conv2D layers are followed by ...