**MobileNet** is a type of convolutional neural network designed for mobile and embedded vision applications. They are based on a streamlined architecture that uses depthwise separable convolutions to build lightweight deep neural networks that can have low latency for mobile and embedded devices.

In the field of fusing multi-spectral and panchromatic images (Pan-sharpening), the impressive effectiveness of deep neural networks has been recently employed to overcome the drawbacks of traditional linear models and boost the fusing accuracy. However, to the best of our knowledge, existing research works are mainly based on simple and flat networks with relatively shallow architecture, which severely limited their performances. In this paper, the concept of residual learning has been introduced to form a very deep convolutional neural network to make a full use of the high non-linearity of deep learning models. By both quantitative and visual assessments on a large number of high quality multi-spectral images from various sources, it has been supported that our proposed model is superior to all mainstream algorithms included in the comparison, and achieved the highest spatial-spectral unified accuracy.

DRPNN

Boosting the accuracy of multi-spectral image pan-sharpening by learning a deep residual network

MobileNetV1

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Smish is an activation function defined as $f(x)=x\cdot \text{tanh}(\ln(1+\sigma(x)))$ where $\sigma(x)$ denotes the sigmoid function. A parameterized version was also described in the form $f(x)=\alpha x\cdot \text{tanh}(\ln(1+\sigma(\beta x)))$.

Paper: Smish: A Novel Activation Function for Deep Learning Methods

Source: https://www.mdpi.com/2079-9292/11/4/540

Source	MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com