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Denoising Autoencoders
- Traditionally, autoencoders minimize some function where is a loss function penalizing for being dissimilar from , such as the norm of their difference.
- A denoising auto encoder (DAE) instead minimizes where is a copy of that has been corrupted by some form of noise. Denoising autoencoders must therefore undo this corruption rather than simply copying theirinput.
- Two assumptions are inherent to this approach: 1)Higher level representations are relatively stable and robust to the corruption of the input; 2) To perform denoising well, the model needs to extract features that capture useful structure in the input distribution.
- In other words, denoising is advocated as a training criterion for learning to extract useful features that will constitute better higher level representations of the input.
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Learn After
Introduction of Denoising Autoencoders
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History of MLPs for Denoising Dates
Training Encoder-Decoder Models with a Denoising Autoencoding Objective
An engineer trains two autoencoder models on a large dataset of clean, high-resolution images. Model A is a standard autoencoder, trained to reconstruct the original images perfectly. Model B is a denoising autoencoder, trained to reconstruct the original clean images from input images that have been intentionally corrupted with random noise (e.g., salt-and-pepper noise). After training, both models are evaluated on their ability to reconstruct a new set of images that have a different, unseen type of corruption (e.g., a slight blur). Based on their training objectives, which model is expected to perform better on this new task, and why?
A key modification to the standard autoencoder training process is the introduction of a 'corruption' step to create a more robust model. Arrange the following steps to accurately describe a single training iteration for this modified approach, which aims to reconstruct an original data point from a noisy version of it.
An autoencoder model is trained on a large dataset of facial images. During each training step, a clean image (
x) is taken, a random rectangular section of it is completely blacked out to create a corrupted version (~x), and the model is tasked with reconstructing the original, clean image (x) from the corrupted input (~x). Which of the following best explains what the model must learn about the data distribution to succeed at this specific task?