Summary of Enot: Expectile Regularization For Fast and Accurate Training Of Neural Optimal Transport, by Nazar Buzun et al.
ENOT: Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport
by Nazar Buzun, Maksim Bobrin, Dmitry V. Dylov
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach for Neural Optimal Transport (NOT) training procedure, called Expectile-Regularised Neural Optimal Transport (ENOT), is introduced. ENOT resolves the main bottleneck of existing NOT solvers by proposing a theoretically justified loss in the form of expectile regularization, which enforces binding conditions on the learning process of dual potentials. This regularization provides an upper bound estimation over the distribution of possible conjugate potentials and makes the learning stable. The proposed method outperforms previous state-of-the-art approaches on Wasserstein-2 benchmark tasks by a large margin (up to 3-fold improvement in quality and up to 10-fold improvement in runtime). Additionally, ENOT shows robustness for varying cost functions on different tasks such as image generation. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of training Neural Optimal Transport (NOT) models is introduced. This approach, called Expectile-Regularised Neural Optimal Transport (ENOT), makes the learning process more stable and accurate. ENOT does this by adding a special type of regularization to the model’s training process. This regularization helps the model make better predictions and takes less time to train. The new method works well on standard tests and can even generate images that are similar to real ones. |
Keywords
* Artificial intelligence * Image generation * Regularization