mhjensen
146 lines
5.0 KiB
HTML
146 lines
5.0 KiB
HTML
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<title>Data Analysis and Machine Learning: Autoencoders</title>
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{'highest level': 2,
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'sections': [('Autoencoders: Overarching view', 2, None, '___sec0'),
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('Simple examples of Autoencoders', 2, None, '___sec1')]}
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<a class="navbar-brand" href="Autoencoders-bs.html">Data Analysis and Machine Learning: Autoencoders</a>
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<!-- navigation toc: --> <li><a href="#___sec0" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
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<p> </p><p> </p><p> </p> <!-- add vertical space -->
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<a name="part0001"></a>
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<h2 id="___sec0" class="anchor">Autoencoders: Overarching view </h2>
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<p>
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Autoencoders are artificial neural networks capable of learning
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efficient representations of the input data (these representations are called codings) without
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any supervision (i.e., the training set is unlabeled). These codings
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typically have a much lower dimensionality than the input data, making
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autoencoders useful for dimensionality reduction.
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<p>
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More importantly, autoencoders act as powerful feature detectors, and
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they can be used for unsupervised pretraining of deep neural networks.
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<p>
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Lastly, they are capable of randomly generating new data that looks
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very similar to the training data; this is called a generative
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model. For example, you could train an autoencoder on pictures of
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faces, and it would then be able to generate new faces. Surprisingly,
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autoencoders work by simply learning to copy their inputs to their
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outputs. This may sound like a trivial task, but we will see that
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constraining the network in various ways can make it rather
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difficult. For example, you can limit the size of the internal
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representation, or you can add noise to the inputs and train the
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network to recover the original inputs. These constraints prevent the
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autoencoder from trivially copying the inputs directly to the outputs,
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which forces it to learn efficient ways of representing the data. In
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short, the codings are byproducts of the autoencoder’s attempt to
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learn the identity function under some constraints.
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<li><a href="._Autoencoders-bs000.html">«</a></li>
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<li><a href="._Autoencoders-bs000.html">1</a></li>
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<li class="active"><a href="._Autoencoders-bs001.html">2</a></li>
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<li><a href="._Autoencoders-bs002.html">3</a></li>
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<li><a href="._Autoencoders-bs002.html">»</a></li>
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