INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings
Amirhossein Kazerouni Reza Azad Alireza Hosseini Dorit Merhof Ulas Bagci
Implicit Neural Representations (INRs) have revolutionized signal
representation by leveraging neural networks to provide continuous and smooth
representations of complex data. However, existing INRs face limitations in
capturing fine-grained details, handling noise, and adapting to diverse signal
types. To address these challenges, we introduce INCODE, a novel approach that
enhances the control of the sinusoidal-based activation function in INRs using
deep prior knowledge. INCODE comprises a harmonizer network and a composer
network, where the harmonizer network dynamically adjusts key parameters of the
activation function. Through a task-specific pre-trained model, INCODE adapts
the task-specific parameters to optimize the representation process. Our
approach not only excels in representation, but also extends its prowess to
tackle complex tasks such as audio, image, and 3D shape reconstructions, as
well as intricate challenges such as neural radiance fields (NeRFs), and
inverse problems, including denoising, super-resolution, inpainting, and CT
reconstruction. Through comprehensive experiments, INCODE demonstrates its
superiority in terms of robustness, accuracy, quality, and convergence rate,
broadening the scope of signal representation. Please visit the project's
website for details on the proposed method and access to the code.