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Meta-Learning Sparse Implicit Neural Representations To address this issue, we propose to leverage a meta-learning approach in combination with network compression under a sparsity constraint, such that it renders a well-initialized sparse parameterization that evolves quickly to represent a set of unseen signals in the subsequent training
Meta-learning Sparse Implicit Neural Representations We call this procedure Meta-SparseINR (Meta-learning Sparse Implicit Neural Representation) For completeness, we provide preliminaries on the meta-learning procedure we use in Section 4 1, and give a full description of the Meta-SparseINR algorithm in Section 4 2
GitHub - jaeho-lee MetaSparseINR: Meta-Learning Sparse Implicit Neural . . . Official PyTorch implementation of "Meta-learning Sparse Implicit Neural Representations" (NeurIPS 2021) by Jaeho Lee*, Jihoon Tack*, Namhoon Lee, and Jinwoo Shin TL;DR: We develop a scalable method to learn sparse neural representations for a large set of signals
Meta-Learning Sparse Implicit Neural Representations This paper presents a method of alternately meta-learning the Implicit Neural Representations (INR) for a set of signals using the MAML method, and pruning the INR using the global magnitude-based pruning method for sparsifying the INR
Meta-leaning Sparse Implicit Neural Representations To address this issue, we propose to leverage a meta-learning approach in combination with network compression under a sparsity constraint, such that it renders a well-initialized sparse parameterization that evolves quickly to represent a set of unseen signals in the subsequent training