Associative Convolutional Layers
This addresses the need for more efficient models in resource-constrained environments like edge devices, though it is an incremental improvement over existing parameter reduction techniques.
The paper tackles the problem of parameter inefficiency in convolutional neural networks (CNNs) for distributed and edge computing by introducing a method that reduces parameters by approximately 2x while maintaining accuracy within 1% of original models on datasets like CIFAR-10 and ImageNet.
Motivated by the necessity for parameter efficiency in distributed machine learning and AI-enabled edge devices, we provide a general and easy to implement method for significantly reducing the number of parameters of Convolutional Neural Networks (CNNs), during both the training and inference phases. We introduce a simple auxiliary neural network which can generate the convolutional filters of any CNN architecture from a low dimensional latent space. This auxiliary neural network, which we call "Convolutional Slice Generator" (CSG), is unique to the network and provides the association between its convolutional layers. During the training of the CNN, instead of training the filters of the convolutional layers, only the parameters of the CSG and their corresponding "code vectors" are trained. This results in a significant reduction of the number of parameters due to the fact that the CNN can be fully represented using only the parameters of the CSG, the code vectors, the fully connected layers, and the architecture of the CNN. We evaluate our approach by applying it to ResNet and DenseNet models when trained on CIFAR-10 and ImageNet datasets. While reducing the number of parameters by $\approx 2 \times$ on average, the accuracies of these networks remain within 1$\%$ of their original counterparts and in some cases there is an increase in the accuracy.