ebms_proposals

Official implementation (PyTorch) of the paper:
Learning Proposals for Practical Energy-Based Regression, 2021 [arXiv] [project].
Fredrik K. Gustafsson, Martin Danelljan, Thomas B. Schön.
We derive an efficient and convenient objective that can be employed to train a parameterized distribution q(y|x; phi) by directly minimizing its KL divergence to a conditional EBM p(y|x; theta). We then employ the proposed objective to jointly learn an effective MDN proposal distribution during EBM training, thus addressing the main practical limitations of energy-based regression. Furthermore, we utilize our derived training objective to learn MDNs with a jointly trained energy-based teacher, consistently outperforming conventional MDN training on four real-world regression tasks within computer vision.

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Code will be released before the end of 2021.