Deep Learning UI Design Patterns of Mobile Apps in 2023 and Beyond

To unleash the full potential of any mobile application, its efficient interaction with the desired audience is necessary. And this interaction takes place through User Interface (UI), and an effective UI holds great power for the sustenance of any mobile app. 

UI design is not just about the product’s appearance but also about how the product functions. It ensures that the call to action and navigation implementation is available in a flawless manner.

There are a lot of User Interface design patterns available for the developers to choose from. A designer can apply them to their User Interface as per the specific requirements.

Some of the common UI design patterns are given below-

• Forgiving Format- Users can enter data in various forms like town, city, village, or zip code.
• Lazy registration- Registrations and filling forms right in the beginning can be off-putting for many users. Therefore what many apps do is that they allow the users to explore their platforms freely before signing up. You must have noticed in popular apps such as Amazon, H&M, etc.
• Breadcrumbs- It provides linked labels for secondary navigation to the users. 
• Progressive disclosure- This is based on reducing cognitive load on users by breaking input demands into sections. It shows only those features that are related to the task at hand.
• Clear primary actions- Clearly, the buttons which prompt actions such as ‘next,’ ‘save,’ ‘submit,’ etc., stand out because of being colored.
• Hover controls- It hides unnecessary information on detailed pages so that users can find relevant information quickly

Machine learning (ML) and deep learning play a key role in designing user interfaces for mobile apps nowadays. In fact, Netflix saves around $1 billion each year because of the way ML has designed its UI.

Following are the 4 ways how to leverage this technology to craft perfect UI-

• Content personalization
• An Adaptive user interface
• Voice-user interfaces
• Image-recognition technology

Designing UI manually is a cumbersome and time-consuming process. Also, the design produced by humans will only be limited and less interactive. But we can semi-automate the process via the advanced deep learning models- Generative Adversarial Networks (GAN) and Recurrent Neural Networks (RNN). Let us understand these through the following two cases-

Producing professional UI designs from simpler drafts of the design method suggested, in this case, reduces the workload of the developer since he wouldn’t need to design from scratch. Suppose you are a developer and you need to design a user interface for an app. And for that purpose, you have come out with a prototype including several wireframes. Each wireframe contains the screen layout, navigational system, and arrangements. But the problem is that it needs to be worked upon right from scratch based on the wireframe model. This can be a huge task for an in-experienced front-end programmer.

Here DeepUI can be of great help in designing a professional interface from the prototype. And the model used is GenUI based on GAN; Generative Adversarial Networks . The two main components of GenUI are-

• UIGenerator– This component aims to generate new (‘fake’ as per GAN) designs resembling the old (‘true’ as per GAN) ones.
• UIDiscriminator– Its purpose is to discriminate between the ‘fake’ and ‘true’ ones. UIGenerator is well programmed to build an interface resembling the repository but with the same structure as the input.

Produce UI designs through descriptions provided by the user and written in natural language.

Here again, the developer’s workload can get reduced by specifying the description of the design he wants to implement. Suppose you are a developer, and this time you need to design a login interface for a mobile app. There are multiple requirements for it, including the id, password, logo, and login methods. But you already have a design from an existing app in your mind.

Now, you can write the description in a natural language such as English. And then, you can search it in DeepUI’s repository containing millions of UI samples as a query. Among the results produced, you can also filter it further using the categories such as apps’ ratings.

Since descriptively describing those models is not easy, you may use NaturalUI. It is a deep learning model which can learn descriptions of UI designs in the natural language. The whole process will make it possible to design an interface using a natural language.

Conclusion

This was how machine learning or, more specifically, deep learning could make developers take a sigh of relief. It doesn’t just reduce the pressure of work but also helps in building interactive interfaces. Consider the categorizing feature of Google photos, for instance. You could find a photo someday selected by Google Photos that you have forgotten ages ago. I am pretty sure this has happened with a majority of you guys. Well, that’s the power of machine or deep learning in designing the user interface.