Auto- Train-Once: Controller Network Guided Automatic Network Pruning from Scratch

Current techniques for deep neural network (DNN) pruning often involve intricate multi-step processes that re-quire domain-specific expertise, making their widespread adoption challenging. To address the limitation, the Only-Train-Once (OTO) and OTOv2 are proposed to eliminate the need for additional fine-tuning steps by directly training and compressing a general DNN from scratch. Never-theless, the static design of optimizers (in OTO) can lead to convergence issues of local optima. In this paper, we proposed the Auto-Train-Once (A TO), an innovative net-work pruning algorithm designed to automatically reduce the computational and storage costs of DNNs. During the model training phase, our approach not only trains the tar-get model but also leverages a controller network as an ar-chitecture generator to guide the learning of target model weights. Furthermore, we developed a novel stochastic gradient algorithm that enhances the coordination between model training and controller network training, thereby im-proving pruning performance. We provide a comprehen-sive convergence analysis as well as extensive experiments, and the results show that our approach achieves state-of-the-art performance across various model architectures (including ResNet18, ResNet34, ResNet50, ResNet56, and MobileNetv2) on standard benchmark datasets (CIFAR-10, CIFAR-100, and ImageNet). The code is available at https: 11 g i thub. comlxidon gwul Auto Train Once.