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Deep Learning: Difference between revisions
→Approximate Local minmax
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<math>\min_{G} \max_{D} f(G,D)</math> is non-convex concave optimization. | <math>\min_{G} \max_{D} f(G,D)</math> is non-convex concave optimization. | ||
If the generator is sufficiently over-parameterized then Sim GDA converges to a global min-max solution. | If the generator is sufficiently over-parameterized then Sim GDA converges to a global min-max solution. | ||
==Flow-based Generative Models== | |||
==Misc== | ==Misc== | ||