Implicit neural representations are a promising new avenue of representing
general signals by learning a continuous function that, parameterized as a
neural network, maps the domain of a signal to its codomain; the mapping from
spatial coordinates of an image to its pixel values, for example. Being capable
of conveying fine details in a high dimensional signal, unboundedly of its
domain, implicit neural representations ensure many advantages over
conventional discrete representations. However, the current approach is
difficult to scale for a large number of signals or a data set, since learning
a neural representation -- which is parameter heavy by itself -- for each
signal individually requires a lot of memory and computations. 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. We empirically demonstrate
that meta-learned sparse neural representations achieve a much smaller loss
than dense meta-learned models with the same number of parameters, when trained
to fit each signal using the same number of optimization steps.

本文提出了一种利用元学习思想和网络压缩技术相结合的方法，以达到在大量数据集上学习稀疏神经表达的目的，并表明与传统的密集神经网络相比，本方法在相同的参数规模下，能够更快地适应一系列未知信号从而使损失更小。