Autoencoder: Difference between revisions

Revision as of 09:09, 5 December 2023

Revision as of 16:52, 8 December 2023

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Denoising autoencoders (DAE) try to achieve a ''good'' representation by changing the ''reconstruction criterion''.<ref name=":0" /><ref name=":4" />

A DAE, orginallyoriginally called a "robust autoassociative network",<ref name=":13"/> is trained by intentionally corrupting the inputs of a standard autoencoder during training. A noise process is defined by a probability distribution <math>\mu_T</math> over functions <math>T:\mathcal X \to \mathcal X</math>. That is, the function <math>T</math> takes a message <math>x\in \mathcal X</math>, and corrupts it to a noisy version <math>T(x)</math>. The function <math>T</math> is selected randomly, with a probability distribution <math>\mu_T</math>.

Given a task <math>(\mu_{ref}, d)</math>, the problem of training a DAE is the optimization problem:<math display="block">\min_{\theta, \phi}L(\theta, \phi) = \mathbb \mathbb E_{x\sim \mu_X, T\sim\mu_T}[d(x, (D_\theta\circ E_\phi \circ T)(x))]</math>That is, the optimal DAE should take any noisy message and attempt to recover the original message without noise, thus the name "denoising"''.''