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Conditional generative modeling via learning the latent space
  • Sameera Ramasinghe, The Australian National University, Australia & Data61-CSIRO
  • Moshiur Farazi, Data61-CSIRO
  • Salman Khan, Mohamed bin Zayed University of Artificial Intelligence
  • Nick Barnes, The Australian National University, Australia
  • Stephen Gould, The Australian National University, Australia
Document Type

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, 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. Copyright © 2020, The Authors. All rights reserved.

Publication Date
  • Computer Vision and Pattern Recognition (cs.CV),
  • Machine Learning (cs.LG)

IR Deposit conditions: non-described

Preprint available on arXiv

Citation Information
S. Ramasinghe, K. Ranasinghe, S. Khan, N. Barnes, and S. Gould, "Conditional Generative Modeling via Learning the Latent Space", 2020, arXiv:2010.03132