Neural Representations have recently been shown to effectively reconstruct a wide range of signals from 3D meshes and shapes to images and videos. We show that, when adapted correctly, neural representations can be used to directly represent the weights of a pre-trained convolutional neural network, resulting in a Neural Representation for Neural Networks (NeRN). Inspired by coordinate inputs of previous neural representation methods, we assign a coordinate to each convolutional kernel in our network based on its position in the architecture, and optimize a predictor network to map coordinates to their corresponding weights. Similarly to the spatial smoothness of visual scenes, we show that incorporating a smoothness constraint over the original network's weights aids NeRN towards a better reconstruction. In addition, since slight perturbations in pre-trained model weights can result in a considerable accuracy loss, we employ techniques from the field of knowledge distillation to stabilize the learning process. We demonstrate the effectiveness of NeRN in reconstructing widely used architectures on CIFAR-10, CIFAR-100, and ImageNet. Finally, we present two applications using NeRN, demonstrating the capabilities of the learned representations.
翻译:最近,我们展示了神经表征,以有效地重建从3D模和形状到图像和视频等各种信号。我们展示了神经表征,以有效地重建从3D模和形状到图像和视频的广泛信号。我们显示,如果适应得当,神经表征可以直接代表一个受过训练的神经神经网络的重量,从而形成神经网络神经代表(NERN)。在以前神经表征方法的协调投入的启发下,我们根据网络中每个神经内核的定位为网络中的每个革命内核指派了一个坐标,并优化了一个预测网,以绘制与其相应的重量相匹配的坐标。与视觉场景的空间平滑一样,我们显示对原始网络重量的平稳限制有助于NERN进行更好的重建。此外,由于预先训练的模型重量的轻微扰动可能导致相当的精度损失,我们利用知识蒸馏领域的技术稳定学习过程。我们展示了NERN在重建CIFAR-10、CIFAR-100和图像网络上广泛使用的建筑结构方面的有效性。最后,我们展示了两个应用NER的应用程序,展示了所学的演示能力。