MoEC: Mixture of Experts Implicit Neural Compression
Jianchen Zhao Cheng-Ching Tseng Ming Lu Ruichuan An Xiaobao Wei He Sun Shanghang Zhang
Emerging Implicit Neural Representation (INR) is a promising data compression
technique, which represents the data using the parameters of a Deep Neural
Network (DNN). Existing methods manually partition a complex scene into local
regions and overfit the INRs into those regions. However, manually designing
the partition scheme for a complex scene is very challenging and fails to
jointly learn the partition and INRs. To solve the problem, we propose MoEC, a
novel implicit neural compression method based on the theory of mixture of
experts. Specifically, we use a gating network to automatically assign a
specific INR to a 3D point in the scene. The gating network is trained jointly
with the INRs of different local regions. Compared with block-wise and
tree-structured partitions, our learnable partition can adaptively find the
optimal partition in an end-to-end manner. We conduct detailed experiments on
massive and diverse biomedical data to demonstrate the advantages of MoEC
against existing approaches. In most of experiment settings, we have achieved
state-of-the-art results. Especially in cases of extreme compression ratios,
such as 6000x, we are able to uphold the PSNR of 48.16.