| Paper Abstract and Keywords |
| Presentation |
2024-02-20 14:30
Optimizing Division Schemes with Mixture of Experts for Medical Data Compression
Jiancheng Zhao, Takefumi Ogawa (Utokyo) |
| Abstract |
(in Japanese) |
(See Japanese page) |
| (in English) |
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. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
Medical data / Compression / INR / MoE / / / / |
| Reference Info. |
ITE Tech. Rep. |
| Paper # |
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| Date of Issue |
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| ISSN |
Online edition: ISSN 2424-1970 |
| Download PDF |
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| Conference Information |
| Committee |
IEICE-ITS IEICE-IE ME AIT MMS |
| Conference Date |
2024-02-19 - 2024-02-20 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Hokkaido Univ. |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
Image Processing, etc. |
| Paper Information |
| Registration To |
IEICE-IE |
| Conference Code |
2024-02-ITS-IE-MMS-ME-AIT |
| Language |
English |
| Title (in Japanese) |
(See Japanese page) |
| Sub Title (in Japanese) |
(See Japanese page) |
| Title (in English) |
Optimizing Division Schemes with Mixture of Experts for Medical Data Compression |
| Sub Title (in English) |
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| Keyword(1) |
Medical data |
| Keyword(2) |
Compression |
| Keyword(3) |
INR |
| Keyword(4) |
MoE |
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| 1st Author's Name |
Jiancheng Zhao |
| 1st Author's Affiliation |
The University of Tokyo (Utokyo) |
| 2nd Author's Name |
Takefumi Ogawa |
| 2nd Author's Affiliation |
The University of Tokyo (Utokyo) |
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| Speaker |
Author-1 |
| Date Time |
2024-02-20 14:30:00 |
| Presentation Time |
15 minutes |
| Registration for |
IEICE-IE |
| Paper # |
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| Volume (vol) |
vol.48 |
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