June 7, 2024, 4:44 a.m. | Aaron Ferber, Arman Zharmagambetov, Taoan Huang, Bistra Dilkina, Yuandong Tian

cs.LG updates on arXiv.org arxiv.org

arXiv:2310.02442v2 Announce Type: replace
Abstract: Deep generative models like GAN and VAE have shown impressive results in generating unconstrained objects like images. However, many design settings arising in industrial design, material science, computer graphics and more require that the generated objects satisfy hard combinatorial constraints or meet objectives in addition to modeling a data distribution. To address this, we propose GenCO, a generative framework that guarantees constraint satisfaction throughout training by leveraging differentiable combinatorial solvers to enforce feasibility. GenCO imposes …

abstract arxiv computer computer graphics constraints cs.lg deep generative models design designs diverse gan generated generative generative models graphics however images industrial material modeling objects replace results science type vae

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