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A Theory for Conditional Generative Modeling on Multiple Data Sources
Created by
Haebom
Author
Rongzhen Wang, Yan Zhang, Chenyu Zheng, Chongxuan Li, Guoqiang Wu
Outline
This paper is the first to provide a rigorous analysis of multisource learning in conditional generative modeling using multisource data. In conditional generative modeling where each condition represents a different data source, we establish a general distribution estimation error boundary on the average total variance distance using bracketing numbers based on conditional maximum likelihood estimation. We show that multisource learning guarantees a sharper boundary than single-source learning when there is a certain similarity between the source distributions and the model is sufficiently expressive. In addition, we formalize the general theory on conditional Gaussian estimation and deep generative models, including autoregressive and flexible energy-based models, by characterizing the bracketing numbers. The results emphasize that the number of sources and the similarity between the source distributions enhance the benefits of multisource learning. To verify the theory, we conduct simulations and experiments, and the code can be found in https://github.com/ML-GSAI/Multi-Source-GM .
We present rigorous analytical results that theoretically support the benefits of multi-source learning.
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Quantitative analysis of the impact of the similarity of source distribution and the number of sources on the effectiveness of multi-source learning.
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Application of theoretical results to conditional Gaussian estimation and various deep generative models (autoregressive, energy-based models).
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Validation of the theory through experimental verification and disclosure of the code.
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Limitations:
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The applicability of the presented theory may be limited to models that can efficiently compute the number of bracketings.
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More extensive experimental validation of the multi-source learning effectiveness of various conditional generative models is needed in real-world applications.
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Further research is needed on how to quantify the similarity between source distributions.
