If you want to run deep learning inference for some embedded system, there are several possiblities now, e.g., Rapberry Pi.
Visual localization aims to estimate the localization, which is usually the the coordinate (orientation and localization) in the world coordindately, given one or multiple images.
In this post, we will introduce some neural networks which are suitable for running on mobile devices.
Generative adversarial network (GAN), since proposed in 2014 by Ian Goodfellow has drawn a lot of attentions. It is consisted of a generator and a discriminator, where the generator tries to generate sample and the discrimiantor tries to discriminate the sample generated by generator from the real ones.
The trained network is typically too large to run efficiently on mobile device. For example, VGG16 used for image classification has more 130 Million parameter (about 600 MB on model size) and requires about 31 billion operations to classify an image, which is way to expensive to be done on mobile.
Besides the convolution operator we already found in AlexNet or VGG16, there are few variations, which will be introduced below. The content of this article is based on reading of An Introduction to different Types of Convolutions in Deep Learning
Here is the comparison of the most popular object detection frameworks.