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Found 2 papers, 1 papers with code
CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations
We represent this reduced manifold using continuously differentiable neural fields, which may train on any and all available numerical solutions of the continuous system, even when they are obtained using diverse methods or discretizations.
Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition
Based on our novel temporal data augmentation algorithms, video recognition performances are improved using only a limited amount of training data compared to the spatial-only data augmentation algorithms, including the 1st Visual Inductive Priors (VIPriors) for data-efficient action recognition challenge.
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