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• W2920527457 abstract "A common assumption in causal modeling posits that the data is generated by a set of independent mechanisms, and algorithms should aim to recover this structure. Standard unsupervised learning, however, is often concerned with training a single model to capture the overall distribution or aspects thereof. Inspired by clustering approaches, we consider mixtures of implicit generative models that ``disentangle'' the independent generative mechanisms underlying the data. Relying on an additional set of discriminators, we propose a competitive training procedure in which the models only need to capture the portion of the data distribution from which they can produce realistic samples. As a by-product, each model is simpler and faster to train. We empirically show that our approach splits the training distribution in a sensible way and increases the quality of the generated samples." @default.
• W2920527457 created "2019-03-11" @default.
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• W2920527457 date "2018-04-30" @default.
• W2920527457 modified "2023-09-27" @default.
• W2920527457 title "Competitive Training of Mixtures of Independent Deep Generative Models" @default.
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• W2920527457 hasPublicationYear "2018" @default.
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