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Date
2021Type
- Conference Paper
ETH Bibliography
yes
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Abstract
We introduce COT-GAN, an adversarial algorithm to train implicit generative models optimized for producing sequential data. The loss function of this algorithm is formulated using ideas from Causal Optimal Transport (COT), which combines classic optimal transport methods with an additional temporal causality constraint. Remarkably, we find that this causality condition provides a natural framework to parameterize the cost function that is learned by the discriminator as a robust (worst-case) distance, and an ideal mechanism for learning time dependent data distributions. Following Genevay et al. (2018), we also include an entropic penalization term which allows for the use of the Sinkhorn algorithm when computing the optimal transport cost. Our experiments show effectiveness and stability of COT-GAN when generating both low- and high-dimensional time-series data. The success of the algorithm also relies on a new, improved version of the Sinkhorn divergence which demonstrates less bias in learning. Show more
Publication status
publishedExternal links
Book title
Advances in Neural Information Processing Systems 33Pages / Article No.
Publisher
CurranEvent
Organisational unit
09727 - Acciaio, Beatrice / Acciaio, Beatrice
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.More
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ETH Bibliography
yes
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