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About Semi-Supervised Learning With Deep Generative
This document summarizes a research paper on using deep generative models for semi-supervised learning. The paper proposes two models: (1) a latent-feature discriminative model that uses the latent space of a deep generative model to provide features for training a classifier, and (2) a generative semi-supervised model that treats class labels as latent variables and generates data from the class and additional latent variables. The paper shows how variational inference can be used to jointly optimize the model and variational parameters for these approaches. It demonstrates state-of-the-art results on benchmark datasets and shows the models can learn to separate data classes from intra-class variability.
Detailed Information
| Author: | ['cubeworm'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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