Conditional Generative Modeling via Learning the Latent Space

• URL: http://arxiv.org/abs/2010.03132v2
• Date: Fri, 9 Oct 2020 03:29:17 GMT
• Title: Conditional Generative Modeling via Learning the Latent Space
• Authors: Sameera Ramasinghe, Kanchana Ranasinghe, Salman Khan, Nick Barnes, and Stephen Gould
• Abstract summary: We propose a novel framework for conditional generation in multimodal spaces. It uses latent variables to model generalizable learning patterns. At inference, the latent variables are optimized to find optimal solutions corresponding to multiple output modes.
• Score: 54.620761775441046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although deep learning has achieved appealing results on several machine learning tasks, most of the models are deterministic at inference, limiting their application to single-modal settings. We propose a novel general-purpose framework for conditional generation in multimodal spaces, that uses latent variables to model generalizable learning patterns while minimizing a family of regression cost functions. At inference, the latent variables are optimized to find optimal solutions corresponding to multiple output modes. Compared to existing generative solutions, in multimodal spaces, our approach demonstrates faster and stable convergence, and can learn better representations for downstream tasks. Importantly, it provides a simple generic model that can beat highly engineered pipelines tailored using domain expertise on a variety of tasks, while generating diverse outputs. Our codes will be released.

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