The Deep Network Designer (see figure) provides a way to use pretrained models including SqueezeNet, Inception-v3, ResNet-101, GoogLeNet, and VGG-19, as well as developing new models.

We provide Matlab implementation of the study called "Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks" These programs are licensed as GNU GPLv3. The current version of ...

Due to the extensively existing complexity and uncertainty of systems, feature extraction based on samples is an important task in controller design. As one of the research hotspots, deep auto-encoder ...

However, the image compression with auto encoder has been found for a small number of the improvements. Therefore, this paper presents a detailed study to demonstrate the image compression algorithm ...

VAEs are a neural network architecture composed of two parts: An encoder that encodes data in a lower-dimensional parameter space. A decoder that reconstructs the input data by mapping the ...

The new Deep Learning Toolbox, which replaces the Neural Network Toolbox, will provide engineers with a framework for designing and implementing deep neural networks. Image processing, computer vision ...

P1451 Introduction: A Pre-trained deep neural network named "DnCNN" for image denoising is available with MATLAB Deep Learning Toolbox. This network is trained with natural images (such as images of ...