Dec 3, 2020 · We propose applying standard meta-learning algorithms to learn the initial weight parameters for these fully-connected networks based on the underlying class ...
A coordinate-based neural representation fθ for a given signal is an MLP (with weights θ) that is optimized to map from an input coordinate x to the signal's ...
We propose applying standard meta-learning algorithms to learn the initial weight parameters for these fully-connected networks based on the underlying class ...
A coordinate-based neural representation fθ for a given signal is an MLP (with weights θ) that is optimized to map from an input coordinate x to the signal's ...
Duration: 3:55
Posted: Dec 3, 2020
Posted: Dec 3, 2020
We found that modifying the weight initialization for these coordinate-based networks drastically changed their convergence behavior during test-time ...
Mar 25, 2021 · We propose applying standard meta-learning algorithms to learn the initial weight parameters for these fully-connected networks based on the ...
Dec 3, 2020 · Standard meta-learning algorithms are proposed to be applied to learn the initial weight parameters for fully-connected coordinate-based ...
nerf-meta is a PyTorch re-implementation of NeRF experiments from the paper "Learned Initializations for Optimizing Coordinate-Based Neural Representations".
We propose applying standard meta-learning algorithms to learn the initial weight parameters for these fully-connected networks based on the underlying class ...