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Diffusion Tree Sampling (DTS) вводит новый подход к управлению выводом в диффузионных моделях, переформулируя процесс как оптимизацию с учетом вознаграждения, вдохновленную поиском по дереву Монте-Карло.

DTS обеспечивает асимптотически точные выборки из целевого распределения при бесконечных развертываниях. Его жадная версия, DTS⋆, реализует глобальный поиск выборок с высоким вознаграждением.

В задачах условной генерации на MNIST и CIFAR-10 DTS достигает FID, сравнимого с лучшими базовыми моделями, при 10-кратной экономии вычислений. В задачах преобразования текста в изображение и завершения языка DTS эффективно находит образцы с высоким вознаграждением, сокращая объем вычислений в 5 раз.

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#unrealneural #вкопилкуэрудита

Diffusion Tree Sampling (DTS) вводит новый подход к управлению выводом в диффузионных моделях, переформулируя процесс как оптимизацию с учетом вознаграждения, вдохновленную поиском по дереву Монте-Карло.

DTS обеспечивает асимптотически точные выборки из целевого распределения при бесконечных развертываниях. Его жадная версия, DTS⋆, реализует глобальный поиск выборок с высоким вознаграждением.

В задачах условной генерации на MNIST и CIFAR-10 DTS достигает FID, сравнимого с лучшими базовыми моделями, при 10-кратной экономии вычислений. В задачах преобразования текста в изображение и завершения языка DTS эффективно находит образцы с высоким вознаграждением, сокращая объем вычислений в 5 раз.
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#unrealneural #вкопилкуэрудита

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