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A Game of Snakes and Gans

Asokan, S and Mohammed, FS and Sekhar Seelamantula, C (2023) A Game of Snakes and Gans. In: UNSPECIFIED.

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Official URL: https://doi.org/10.1109/ICASSP49357.2023.10095903


Generative adversarial networks (GANs) comprise generator and discriminator networks trained adversarially to learn the underlying distribution of a dataset. Recently, we have shown that the optimal GAN discriminator can be obtained in closed-form as the solution to the Poisson partial differential equation (PDE). While existing approaches either train a network or solve the PDE in closed-form, we propose training the generator through the gradient field of the optimal discriminator. In this paper, we establish a connection between active contour models (snakes) and GANs. We evolve a set of snake points over the gradient field of radial basis function (RBF) Coulomb GAN. The generator is then trained to follow the trajectory of the snake. The proposed approach benefits from both the sample diversity seen in flow-based approaches and the fast sampling capability of GANs. Experimental validation on 2-D synthetic data shows that the proposed approach leads to accelerated convergence, compared against the baseline approaches that either employ network or kernel-based discriminators. © 2023 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 > Electrical Engineering
Date Deposited: 04 Mar 2024 09:21
Last Modified: 04 Mar 2024 09:21
URI: https://eprints.iisc.ac.in/id/eprint/84287

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