mhjensen
54 lines
2.6 KiB
Plaintext
54 lines
2.6 KiB
Plaintext
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"<!-- dom:TITLE: Data Analysis and Machine Learning: Autoencoders -->\n",
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"# Data Analysis and Machine Learning: Autoencoders\n",
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"<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University -->\n",
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"<!-- Author: --> \n",
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"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
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"\n",
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"Date: **Jan 1, 2019**\n",
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"\n",
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"Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"## Autoencoders: Overarching view\n",
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"\n",
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"Autoencoders are artificial neural networks capable of learning\n",
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"efficient representations of the input data (these representations are called codings) without\n",
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"any supervision (i.e., the training set is unlabeled). These codings\n",
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"typically have a much lower dimensionality than the input data, making\n",
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"autoencoders useful for dimensionality reduction. \n",
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"\n",
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"More importantly, autoencoders act as powerful feature detectors, and\n",
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"they can be used for unsupervised pretraining of deep neural networks.\n",
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"\n",
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"Lastly, they are capable of randomly generating new data that looks\n",
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"very similar to the training data; this is called a generative\n",
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"model. For example, you could train an autoencoder on pictures of\n",
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"faces, and it would then be able to generate new faces. Surprisingly,\n",
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"autoencoders work by simply learning to copy their inputs to their\n",
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"outputs. This may sound like a trivial task, but we will see that\n",
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"constraining the network in various ways can make it rather\n",
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"difficult. For example, you can limit the size of the internal\n",
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"representation, or you can add noise to the inputs and train the\n",
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"network to recover the original inputs. These constraints prevent the\n",
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"autoencoder from trivially copying the inputs directly to the outputs,\n",
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"which forces it to learn efficient ways of representing the data. In\n",
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"short, the codings are byproducts of the autoencoder’s attempt to\n",
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"learn the identity function under some constraints.\n",
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"\n",
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"## Simple examples of Autoencoders"
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