Articles Fritz has written:

Snapchat, FaceApp, and the necessary lessons of data privacy with mobile machine learning

Articles

If you’re even remotely plugged into the tech world, you’d have been hard-pressed to miss a couple viral summer trends, both involving AI-powered photo transformations.

Here’s the gist. Snapchat caught fire and soared past all Q2 estimates, in large part because of their rollout out of popular gender-swap and baby-face Lenses. And soon after, FaceApp took the internet by storm when its old-age filter went viral. This has led to millions of users, including prominent celebrities, showing off what they’d look like in 40 years or as members of a different gender.

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Image Classification on Android with TensorFlow Lite and CameraX

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TensorFlow Lite is the lightweight version of TensorFlow Mobile. It’s here to unleash the machine learning power on your smartphones while ensuring that the model binary size isn’t too big and there’s low latency. Additionally, it also supports hardware acceleration using the Neural Networks API and is destined to run 4X faster with GPU support.

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Body Segmentation in the Browser with TensorFlow.js

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Learning and implementing different AI-powered apps using the TensorFlow.js library empowers you to do so many amazing things with ML in the browser.

This tutorial is the latest in my series using TensorFlow.js for machine learning and implementing those models in React apps. Here, we’ll learn about another TensorFlow library that helps with body segmentation.

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Semantic and Instance Segmentation on iOS Using a Flask API — DeepLabV3+ and Mask R-CNN

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Computer Vision — iOS Introduction

Let say you have an image, and you want to distinguish objects of interest— or in other words, find suitable local characteristics to distinguish them from other objects or from the background. This is called image segmentation or semantic segmentation.

When we segment a target object, we know which pixel belongs to which object. The image is divided into regions and the discontinuities serve as borders between the regions. One can also analyze the shape of objects using various morphological operators.

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Running Create ML Style Transfer Models in an iOS Camera Application

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Style transfer is a very popular deep learning task that lets you change an image’s composition by applying the visual style of another image.

From building artistic photo editors to giving a new look to your game designs through state-of-the-art themes, there are plenty of amazing things you can build with neural style transfer models. It’s also can be handy or data augmentation.

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Fritz AI Models Included in Lens Studio 3.4 Asset Library

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When Snapchat’s Lens Studio released SnapML as a core part of their 3rd major platform update (3.0) back in June of 2020, our antennae immediately raised at the introduction of a new mobile platform for machine learning models.

We seized the opportunity, got to work, and less than a year later, a collection of our machine learning models have been baked into Lens Studio 3.4, the latest release of Snapchat’s flagship design tool. That’s right—Lens Creators can now directly access ML models designed and built specifically for Lens Studio by our team of experts!

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