I'll dedicate this rambling thread of words to the linguist Ray Jackendoff who studies semantics + computational theory of mind. This paragraph from his monograph debating the merits of studying the details individual words vs patters across many words is one of my favourites.
And my next step with these is finding ways of limiting the color palette. If you enjoy this work and would like to contribute (and are comfortable with pytorch) feel free to offer up coding suggestions in this github issues thread. github.com/dribnet/clipit…
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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.👇
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.