MOMENTUM-IMBUED LANGEVIN DYNAMICS (MILD) FOR FASTER SAMPLING

Shetty, N and Bandla, M and Neema, N and Asokan, S and Seelamantula, CS (2024) MOMENTUM-IMBUED LANGEVIN DYNAMICS (MILD) FOR FASTER SAMPLING. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024, 14 - 19 April 2024, Seoul, pp. 6635-6639.

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Abstract

Score-based generative models have emerged as the state-of-the-art in generative modeling. In this paper, we introduce a novel sampling scheme that can be combined with pre-trained score-based diffusion models to speed up sampling by a factor of two to five in terms of the number of function evaluations (NFEs) with a superior Fréchet Inception distance (FID), compared to Annealed Langevin dynamics in noise-conditional score network (NCSN) and improved noise-conditional score network (NCSN++). The proposed sampling algorithm is inspired by momentum-based accelerated gradient descent used in convex optimization techniques. We validate the sampling efficiency of the proposed algorithm in terms of FID on CIFAR-10 and CelebA datasets. © 2024 IEEE.

Item Type:	Conference Paper
Publication:	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation Division of Electrical Sciences > Electrical Engineering
Date Deposited:	19 Aug 2024 10:25
Last Modified:	19 Aug 2024 10:25
URI:	http://eprints.iisc.ac.in/id/eprint/85472

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