用动态图建模神经网络推理过程，提升隐式表示分类效果
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space
- 将网络权重视为时序动态图，捕捉逐层推理过程
- 在CIFAR-100-INR上实现超主流方法10%的准确率提升
- 适合研究隐式神经表示与模型内部表征的学者
基于神经网络作为隐式数据表示的快速发展，针对其他神经网络权重空间的分析与处理方法受到广泛关注。然而，高效处理高维权重空间仍具挑战性，现有方法常忽视神经网络推理中逐层处理的时序特性。本文提出一种新方法，利用动态图表示神经网络参数，捕捉推理过程中的时间动态。所提出的动态神经图编码器（DNG-Encoder）能有效处理此类图结构，保留神经处理的序列性。此外，我们基于DNG-Encoder构建INR2JLS（隐式神经表示到联合潜空间映射），以支持下游任务，如隐式神经表示（INRs）分类。实验表明，该方法在多项任务中表现优异，在CIFAR-100-INR数据集上相较当前最优方法准确率提升约10%。
原文摘要 · Abstract (English)
The rapid advancements in using neural networks as implicit data representations have attracted significant interest in developing machine learning methods that analyze and process the weight spaces of other neural networks. However, efficiently handling these highdimensional weight spaces remains challenging. Existing methods often overlook the sequential nature of layer-by-layer processing in neural network inference. In this work, we propose a novel approach using dynamic graphs to represent neural network parameters, capturing the temporal dynamics of inference. Our Dynamic Neural Graph Encoder (DNG-Encoder) processes these graphs, preserving the sequential nature of neural processing. Additionally, we also leverage DNG-Encoder to develop INR2JLS (Implicit Neural Representation to Joint Latent Space) for facilitate downstream applications, such as classifying Implicit Neural Representations (INRs). Our approach demonstrates significant improvements across multiple tasks, surpassing the state-of-the-art INR classification accuracy by approximately 10% on the CIFAR-100-INR.
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