CVL-UESTC/IGA-INR
ICML2025-Inductive Gradient Adjustment for Spectral Bias in Implicit Neural Representations
This project offers a method to improve the quality of images and 3D shapes generated by Implicit Neural Representations (INRs). It takes raw data for images or 3D models and helps INRs learn fine details like textures and edges more effectively. This is particularly useful for researchers and practitioners working with neural networks to reconstruct or generate high-fidelity visual content.
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Use this if you are training Implicit Neural Representations (INRs) and need to capture high-frequency details, such as sharp textures and intricate edges, more accurately in your generated images or 3D models.
Not ideal if your primary goal is general neural network training that doesn't specifically involve Implicit Neural Representations for high-fidelity content generation.
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May 31, 2025
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