Uizard Research

What comes to mind when you think of exceptional user experience? If you're a designer or a product owner, you probably have an idea of what makes great design; you might even have your own UI design playbook for crafting the perfect apps or websites every time you start a new project.

The truth is, refining your UI design to ensure you give your audience the best possible experience is a tricky task. Great UI design isn’t just about how your design looks, what colors you use, or the tone of voice you employ across your content...

If you have ever tried creating a user interface, you probably quickly realized that design is hard. Choosing the right colors, using fonts that match, making your layout balanced… And all of that while keeping your users’ needs in mind! Can we somehow reduce all of this complexity, allowing everyone to design, even if they don’t know about spacing rules or color contrast theory? Wouldn’t it be nice if software could help you with these?

This problem is not new. This is a long-standing problem that the Human Computer Interaction (HCI) community has been working towards for years...

How can we modify transformers for UI-centric tasks? Layout Understanding is a sub-field of AI that enables machines to better process the semantics and information within layouts such as user interfaces (UIs), text documents, forms, presentation slides, graphic design compositions, etc.

Companies already invest extensive resources into their web/mobile applications' UI and user experience (UX), with Fast Company reporting that every $1 spent on UX can return between 2-100x on its investment.

How do you enable people to easily design beautiful mobile and web apps when they have no design background? Easy: you just throw some neural networks at the problem! Well, kinda...

You might have heard of Uizard already through our early machine learning research pix2code or our technology for turning hand-drawn sketches into interactive app prototypes. Today, I would like to talk about one of our new AI-driven features: automatic theme generation.

Semi-Supervised Learning algorithms have shown great potential in training regimes when access to labeled data is scarce but access to unlabeled data is plentiful.

In this post, we discuss how Semi-supervisedlearning approaches can be useful for machine learning production environmentsand the lessons we've learned using them at Uizard.

Semi-Supervised Learning (SSL) algorithms have shown great potential in training regimes when access to labeled data is scarce but access to unlabeled data is plentiful.

However, our experiments illustrate several shortcomings that prior SSL algorithms suffer from. In particular, poor performance when unlabeled and labeled data distributions differ. To address these observations, we develop RealMix, which...