WebThen, an efficient network with smaller model size and computational complexity is trained using the generated data and the teacher network, simultaneously. Efficient student networks learned using the proposed Data-Free Learning (DFL) method achieve 92.22% and 74.47% accuracies without any training data on the CIFAR-10 and CIFAR-100 … WebOct 1, 2024 · Request PDF On Oct 1, 2024, Hanting Chen and others published Data-Free Learning of Student Networks Find, read and cite all the research you need on …
Data-Free Learning of Student Networks - arXiv
WebHello, I'm Ahmed, a graduate of computer science and an M.Tech in Data Science student at IIT Madras with a passion for using data to drive … WebThen, an efficient network with smaller model size and computational complexity is trained using the generated data and the teacher network, simultaneously. Efficient student … ray stata net worth
(PDF) Data-Free Learning of Student Networks - ResearchGate
WebData-Free Learning of Student Networks. H Chen, Y Wang, C Xu, Z Yang, C Liu, B Shi, C Xu, C Xu, Q Tian. IEEE International Conference on Computer Vision, 2024. 245: 2024: Evolutionary generative adversarial networks. C Wang, C Xu, X Yao, D Tao. IEEE Transactions on Evolutionary Computation 23 (6), 921-934, 2024. 242: WebOct 19, 2024 · This work presents a method for data-free knowledge distillation, which is able to compress deep neural networks trained on large-scale datasets to a fraction of their size leveraging only some extra metadata to be provided with a pretrained model release. Recent advances in model compression have provided procedures for compressing … WebAug 1, 2024 · In this study, we propose a novel data-free knowledge distillation method that is applicable to regression problems. Given a teacher network, we adopt a generator network to transfer the knowledge in the teacher network to a student network. We simultaneously train the generator and student networks in an adversarial manner. raystat-control-10