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Denoising Autoencoders: Learn By Fixing Broken Data
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YouTubePaper Bytes
Denoising Autoencoders: Learn By Fixing Broken Data
In 2010, Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and their team published a landmark paper that flipped deep learning training on its head with a counterintuitive insight: forcing networks to fix corrupted data produces far better representations than training on clean inputs. Their stacked denoising autoencoder (SDAE) solved the ...
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