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 .

翻译：隐式神经表示（INR）或神经场已成为编码图像和辐射场等多媒体信号并在保持高质量的同时广泛采用的框架。近期，Instant-NGP提出的可学习特征网格通过用多分辨率特征向量查找表和更小规模的神经网络替代大型神经网络，显著加速了INR的训练与采样过程。然而，这些特征网格以大量内存消耗为代价，这可能成为存储与流式传输应用的瓶颈。本文提出SHACIRA——一种简单而有效的任务无关框架，用于压缩此类特征网格，无需额外的后处理剪枝/量化阶段。我们通过量化潜在权重重新参数化特征网格，并在潜在空间中应用熵正则化，从而在多个领域实现高压缩比。在包含图像、视频和辐射场的多样化数据集上的定量与定性结果表明，我们的方法无需依赖大型数据集或领域特定启发式规则，即可超越现有INR方法。项目页面访问地址为http://shacira.github.io 。