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Version du 15 décembre 2020 à 18:10
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Disentangled Representation Learning
Disentangled representation is an unsupervised learning technique that breaks down, or disentangles, each feature into narrowly defined variables and encodes them as separate dimensions. The goal is to mimic the quick intuition process of a human, using both “high” and “low” dimension reasoning. For example, in a predictive network processing images of people, “higher dimensional” features such as height and clothing would be used to determine sex. In a generative network version of that model designed to produce images of people from a stock photo database, these would be broken down into separate, lower dimensional features. Such as: total height of each person, length of arms and legs, type of shirt, type of pants, type of shoe, etc…
Contributeurs: Marie Alfaro, wiki
