mhjensen
326 lines
11 KiB
HTML
326 lines
11 KiB
HTML
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<section>
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<!-- ------------------- main content ---------------------- -->
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<center><h1 style="text-align: center;">Data Analysis and Machine Learning: Autoencoders</h1></center> <!-- document title -->
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<p>
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<!-- author(s): Morten Hjorth-Jensen -->
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<center>
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<b>Morten Hjorth-Jensen</b> [1, 2]
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</center>
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<p> <br>
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<!-- institution(s) -->
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<center>[1] <b>Department of Physics, University of Oslo</b></center>
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<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
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<br>
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<p> <br>
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<center><h4>Jan 1, 2019</h4></center> <!-- date -->
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<br>
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<center style="font-size:80%">
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<!-- copyright --> © 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
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</center>
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</section>
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<section>
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<h2 id="___sec0">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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</section>
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<section>
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<h2 id="___sec1">Simple examples of Autoencoders </h2>
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</section>
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