Abstract
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.
Original language | English |
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State | Published - 1 Jan 2023 |
Event | 11th International Conference on Learning Representations, ICLR 2023 - Kigali, Rwanda Duration: 1 May 2023 → 5 May 2023 |
Conference
Conference | 11th International Conference on Learning Representations, ICLR 2023 |
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Country/Territory | Rwanda |
City | Kigali |
Period | 1/05/23 → 5/05/23 |
ASJC Scopus subject areas
- Language and Linguistics
- Computer Science Applications
- Education
- Linguistics and Language