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Crafting Artistic Images with Embedded AI with Alberto Ancilotto of FBK
Manage episode 456398157 series 3574631
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Unlock the secrets of neural style transfer on microcontrollers with our special guest, Alberto Ancilotto of FBK, as he explores a groundbreaking approach to image generation on low-power devices. Discover how this innovative technique allows us to combine the content of one image with the artistic style of another, transforming simple visuals into unique masterpieces—like turning a regular cat photo into a Van Gogh-inspired work of art. Alberto introduces Xinet, a cutting-edge convolutional neural network designed to perform these creative tasks efficiently on embedded platforms. Gain insight into the process of optimizing performance by evaluating CNN operators for energy efficiency and adapting networks for a variety of devices, from the smallest microcontrollers to advanced TPUs and accelerators.
We dive deep into the collaboration between Clip and style transfer networks, enhancing the precision of semantic representation in generated images. Witness the impressive capabilities of this technology through real-world examples, such as generating images in just 60 milliseconds on the STM32N6 microcontroller. Experience the advanced applications in video anonymization, where style transfer provides a superior alternative to traditional blurring methods, altering appearances while maintaining action consistency. Alberto also addresses the broader implications of anonymization technology in public spaces, including privacy protection and GDPR compliance, while maintaining artistic integrity. Join us as we tackle audience questions about model parameters, deployment flexibility, and the exciting potential of this technology across various sectors.
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Hoofdstukken
1. Crafting Artistic Images with Embedded AI with Alberto Ancilotto of FBK (00:00:00)
2. Neural Style Transfer for Microcontrollers (00:00:23)
3. Neural Style Transfer on Embedded Devices (00:15:50)
4. Impacts of Anonymization Technology in Public (00:24:09)
20 afleveringen
Delen
