Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal Transport Relaxations with Application to Wasserstein GANs   We propose a family of relaxations of the optimal transport problem which
regularize the problem by introducing an additional minimization step over a
small region around one of the underlying transporting measures. The type of
regularization that we obtain is related to smoothing techniques studied in the
optimization literature. When using our approach to estimate optimal transport
costs based on empirical measures, we obtain statistical learning bounds which
are useful to guide the amount of regularization, while maintaining good
generalization properties. To illustrate the computational advantages of our
regularization approach, we apply our method to training Wasserstein GANs. We
obtain running time improvements, relative to current benchmarks, with no
deterioration in testing performance (via FID). The running time improvement
occurs because our new optimality-based threshold criterion reduces the number
of expensive iterates of the generating networks, while increasing the number
of actor-critic iterations.

22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph