WebDAFL: Data-Free Learning of Student Networks. This code is the Pytorch implementation of ICCV 2024 paper DAFL: Data-Free Learning of Student Networks. We propose a novel framework for training efficient deep neural networks by exploiting generative adversarial networks (GANs). WebApr 2, 2024 · Data-Free Learning of Student Networks. Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors. Most existing deep neural network compression and speed-up methods are very …
Yunhe Wang
WebData-Free-Learning-of-Student-Networks / DAFL_train.py Go to file Go to file T; Go to line L; Copy path Copy permalink; This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Cannot retrieve contributors at this time. WebData-free learning for student networks is a new paradigm for solving users' anxiety caused by the privacy problem of using original training data. Since the architectures of … how to store items in a damp basement
Data-Free Learning of Student Networks - IEEE Xplore
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 … WebOct 1, 2024 · Then, an efficient network with smaller model size and computational complexity is trained using the generated data and the teacher network, simultaneously. … WebOct 27, 2024 · 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 … how to store iris tubers