Efficient Compression of Implicit Neural Representations using Decoder-Only Hypernetworks

Основные понятия

A decoder-only hypernetwork framework is proposed to efficiently compress implicit neural representations without requiring offline training on a target signal class.

Аннотация

The paper introduces a novel "decoder-only" hypernetwork framework for compressing implicit neural representations (INRs). Unlike previous hypernetwork approaches for INRs, the proposed method does not require offline training on a target signal class. Instead, it can be optimized at runtime using only the target data instance.

The key aspects of the method are:

1. Decoder-only architecture: The hypernetwork acts as a decoder-only module, generating the weights of a target INR architecture from a low-dimensional latent code. This avoids the need for a separate encoding step conditioned on a training dataset.

2. Random projection decoder: The hypernetwork uses a fixed random projection to map the latent code to the target network weights, enabling a highly compact representation.

3. Runtime optimization: The latent code is optimized at runtime to approximate the target INR, without requiring any offline training data.

The authors demonstrate the effectiveness of this approach on image compression and occupancy field representation tasks. Compared to prior methods like COIN, the decoder-only hypernetwork achieves improved rate-distortion performance while allowing smooth control of the bit-rate by varying the latent code dimension, without the need for neural architecture search.

The paper also discusses interesting properties of the decoder-only hypernetwork, such as its ability to incorporate positional encoding without increasing the parameter count, and a method to directly project a pre-trained INR into the hypernetwork framework.

Статистика

The paper does not provide any specific numerical data or metrics in the main text. The key results are presented through rate-distortion curves and qualitative comparisons.

Цитаты

"We propose to use a novel run-time decoder-only hypernetwork – that uses no offline training data – to better model this cross-layer parameter redundancy."
"By directly changing the dimension of a latent code to approximate a target implicit neural architecture, we provide a natural way to vary the memory footprint of neural representations without the costly need for neural architecture search on a space of alternative low-rate structures."

Ключевые выводы из

D'OH

by Cameron Gord... в arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19163.pdf

Дополнительные вопросы

How can the decoder-only hypernetwork framework be extended to other types of implicit neural representations beyond images and occupancy fields, such as neural radiance fields or point clouds

The decoder-only hypernetwork framework can be extended to other types of implicit neural representations, such as neural radiance fields or point clouds, by adapting the architecture and training process to suit the specific characteristics of these representations. For neural radiance fields, which model the appearance of 3D scenes, the decoder-only hypernetwork can be designed to predict radiance values at sampled points in 3D space. By training the hypernetwork on radiance field data instances, it can learn to generate the parameters necessary to represent complex lighting and material properties in a compact manner. Similarly, for point clouds, the hypernetwork can be tailored to predict attributes or features at individual points, enabling the efficient encoding of spatial data in a compressed form. By adjusting the latent code dimension and the decoding strategy to match the requirements of these representations, the decoder-only hypernetwork can effectively capture the underlying structures and patterns present in neural radiance fields and point clouds.

What are the potential limitations or drawbacks of the random projection-based decoder compared to more expressive decoding architectures, and how could these be addressed

The random projection-based decoder in the decoder-only hypernetwork framework may have limitations compared to more expressive decoding architectures in terms of flexibility and representational power. One potential drawback is the fixed nature of the random projection matrices, which may not capture complex relationships or patterns in the data as effectively as learned decoding functions. To address this limitation, one approach could be to introduce more sophisticated decoding mechanisms, such as learned non-linear transformations or attention mechanisms, into the hypernetwork architecture. By incorporating these elements, the decoder can adapt to the specific characteristics of the data and capture intricate dependencies between features more accurately. Additionally, exploring different types of random projections or incorporating structured random matrices could enhance the expressive capacity of the decoder while maintaining the benefits of a compact representation.

Given the ability to directly project a pre-trained INR into the hypernetwork framework, how could this be leveraged to enable efficient fine-tuning or adaptation of INRs to new data or tasks

The ability to directly project a pre-trained implicit neural representation (INR) into the hypernetwork framework opens up opportunities for efficient fine-tuning and adaptation to new data or tasks. By leveraging the pre-trained INR as a starting point, the hypernetwork can quickly learn to generate the parameters needed to represent the new data instances or tasks without extensive training on a large dataset. This approach enables rapid adaptation of the INR to different domains or applications, saving time and computational resources. Furthermore, fine-tuning the hypernetwork on the new data or tasks allows for the refinement of the generated parameters to better fit the specific requirements, leading to improved performance and generalization. Overall, this strategy facilitates the seamless integration of pre-trained models into the decoder-only hypernetwork framework, enhancing the flexibility and versatility of the system.