  Implicit Neural Representations (INR) or neural fields have emerged as a
popular framework to encode multimedia signals such as images and radiance
fields while retaining high-quality. Recently, learnable feature grids proposed
by Instant-NGP have allowed significant speed-up in the training as well as the
sampling of INRs by replacing a large neural network with a multi-resolution
look-up table of feature vectors and a much smaller neural network. However,
these feature grids come at the expense of large memory consumption which can
be a bottleneck for storage and streaming applications. In this work, we
propose SHACIRA, a simple yet effective task-agnostic framework for compressing
such feature grids with no additional post-hoc pruning/quantization stages. We
reparameterize feature grids with quantized latent weights and apply entropy
regularization in the latent space to achieve high levels of compression across
various domains. Quantitative and qualitative results on diverse datasets
consisting of images, videos, and radiance fields, show that our approach
outperforms existing INR approaches without the need for any large datasets or
domain-specific heuristics. Our project page is available at
http://shacira.github.io .
