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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Disorder-Invariant Implicit Neural Representation.

Hao Zhu, Shaowen Xie, Zhen Liu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|February 15, 2024

Summary

This summary is machine-generated.

Disorder-Invariant Implicit Neural Representation (DINER) enhances signal attribute modeling by using hash-tables to overcome spectral bias. This method improves performance across various tasks and network architectures.

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Area of Science:

Artificial Intelligence
Machine Learning
Signal Processing

Background:

Implicit Neural Representations (INRs) model signal attributes via coordinate functions, proving effective for inverse problems.
A key limitation of INRs is spectral bias during network training, restricting their expressive power.

Purpose of the Study:

To address the spectral bias limitation in Implicit Neural Representations.
To propose a novel method, Disorder-Invariant Implicit Neural Representation (DINER), for enhanced signal representation.

Main Methods:

Augmenting traditional INR backbones with a hash-table to create DINER.
Re-arranging input signal coordinates to project them into a consistent distribution using the hash-table.
Varying the hash-table width to control expressive power, corresponding to different geometrical elements (1D, 2D, 3D).

Main Results:

DINER significantly alleviates spectral bias by enabling better signal modeling.
The expressive power of DINER scales with hash-table width, covering more geometrical elements.
Demonstrated generalization across different INR backbones (MLP, SIREN) and tasks (image/video representation, phase retrieval, refractive index recovery, neural radiance field optimization).

Conclusions:

DINER offers superior performance in both quality and speed compared to state-of-the-art algorithms.
The proposed method effectively overcomes spectral bias in INRs.
DINER presents a versatile and powerful approach for various signal representation and inverse problems.