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SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks

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hardmaru: SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks If we train a neural net to classify CIFAR10 photos but also give it unlabelled QuickDraw doodles, how well can it classify these doodles? https://arxiv.org/abs/1912.11570 https://t.co/YwjFcmTR17

3 replies, 458 likes


Melanie Mitchell: If a neural network learns to recognize objects in photos, can it also recognize human-drawn sketches of these objects? Sort of. Cool paper exploring this question!

3 replies, 143 likes


Pablo Samuel Castro: Very cool idea and paper! The ability to recognize the most salient features in objects, shared across images, 3D renderings, and sketches, could likely provide a step towards more general and reusable representational primitives.

1 replies, 80 likes


roadrunner01: SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks pdf: https://arxiv.org/pdf/1912.11570.pdf abs: https://arxiv.org/abs/1912.11570 https://t.co/v9m2zqW6XZ

0 replies, 23 likes


NIDHAL SELMI - نضال السالمي: A neural net trained on photos only gets 59% accuracy with doodles (11% is random baseline). Trained on doodles directly gives 87%. Sketches are hard because they are human abstractions. This is a good path towards reasoning since abstractions are noise/detail filters.

0 replies, 11 likes


akira: https://arxiv.org/abs/1912.11570 Although humans can correctly classify even sketches that losses details, the current ML model not. In order to evaluate such generalization performance, the authors propose a task on new illustration SketchTransfer dataset with model trained on CIFAR10. https://t.co/wAEgsi4Ivq

0 replies, 2 likes


Content

Found on Feb 12 2020 at https://arxiv.org/pdf/1912.11570.pdf

PDF content of a computer science paper: SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks