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Poster

Accelerating Neural Field Training via Soft Mining

Shakiba Kheradmand · Daniel Rebain · Gopal Sharma · Hossam Isack · Abhishek Kar · Andrea Tagliasacchi · Kwang Moo Yi

Arch 4A-E Poster #52
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Fri 21 Jun 10:30 a.m. PDT — noon PDT

Abstract:

We present an approach to accelerate Neural Field training by efficiently selecting sampling locations. While Neural Fields have recently become popular, it is often trained by uniformly sampling the training domain, or through handcrafted heuristics. We show that improved convergence and final training quality can be achieved by a soft mining technique based on importance sampling: rather than either considering or ignoring a pixel completely, we weigh the corresponding loss by a scalar. To implement our idea we use Langevin Monte-Carlo sampling. We show that by doing so, regions with higher error are being selected more frequently, leading to more than 2x improvement in convergence speed.

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