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At the recent #neurips4creativity panel someone asked what areas of machine learning were recommend for newcomers, and I suggested exploring atypical algos such as image segmentation and how they might be repurposed.
let me explain further with using some of my students' work.👇
let me explain further with using some of my students' work.👇
Semantic Image Segmentation is arguably one of the more mundane success stories of modern computer vision. The task is to label each pixel with a label - @jeremyjordan provides a good overview if you are unfamiliar: jeremyjordan.me/semantic-segme…
This past year I introduced it to my @vicuniwgtn design 2nd year undergraduates as part of a digital photography assignment. It seemed completely natural to students that (of course!) images can be decomposed into not only Red, Green, and Blue but also imperfect semantic labels.
#NeurIPS2018 is over! Time to wrap up!
In this thread I'll share what I found the most interesting in the field of ML and creativity.
Everything below worth your time IMO!
In this thread I'll share what I found the most interesting in the field of ML and creativity.
Everything below worth your time IMO!
First day, the talk by @viegasf & @wattenberg about DataVisualisation was excellent: theoretical and practical.
static.googleusercontent.com/media/research…
static.googleusercontent.com/media/research…
Fernanda Viega co-lead the People + AI Research team for Google Cambridge. She makes Art & Design Projects too. More here: fernandaviegas.com
