The increasing interest in filter pruning of convolutional neural networks stems from its inherent ability to effectively compress and accelerate these networks. Currently, filter pruning is mainly divided into two schools: norm-based and relation-based. These methods aim to selectively remove the least...
In addition, we also adopted traditional CAE, Gate Recurrent Unit based Auto-Encoder (GRU-AE) and TFA-GRU-AE models for comparison with the TFA-CAE model. The PyTorch deep learning framework (version 1.11) is employed to construct and train all the above models. Moreover, the adaptive ...
This is an official repository of Generating 3D Faces using Convolutional Mesh Autoencoders[Project Page][Arxiv]UPDATE : Thank you for using and supporting this repository over the last two years. This will no longer be maintained. Alternatively, please use:...
Or Litany, Alex Bronstein, Michael Bronstein, Deformable Shape Completion with Graph Convolutional Autoencoders, 2017 Ilya Kostrikov, Joan Bruna, Surface Networks, 2017 Martin Simonovsky, Nikos Komodakis, GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders, ICLR 2018 编辑于 ...
本节首先介绍实验中采用的数据集、评价准则以及部分相关的实验细节;然后将所提方法与一些现有的基于深度学习的SAR目标识别方法的性能进行比较,以验证所提方法的有效性;最后进行了模型分析,包括预设的ASC核的相关参数对识别性能的影响以及不同训练样本数对识别性能的影响。所有实验代码均基于Ubuntu系统下的Pytorch进行编写。
For example: cd examples/Assamese_handwriting python VGGplus.py Setup Tested with PyTorch 1.3, CUDA 10.0, and Python 3.3 withConda. conda install pytorch torchvision cudatoolkit=10.0 -c pytorch # See https://pytorch.org/get-started/locally/ git clone git@github.com:facebookresearch/SparseConvNet...
et al. UC-Net: Uncertainty inspired RGB-D saliency detection via conditional variational autoencoders. In IEEE Conference on Computer Vision and Pattern Recognition (2020). Chen, H. & Li, Y. Progressively complementarity-aware fusion network for RGB-D salient object detection. In IEEE Conf. ...
Our proposed CCNet is implemented in Pytorch, which is trained for 300 epochs on a single NVIDIA Tesla T4 GPU. The Adam optimizer is used with default values. The initial learning rate is set as 1e−4 for Adam optimizer and the batch size is 10. The poly learning rate policy is used...
Its deep learning framework is implemented using PyTorch 0.3.1 to enable GPU application for training/testing. The source code is available at https://github.com/bioinform/neusomatic under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license. The results in this paper ...
Using the PyTorch package, the models were trained with NVIDIA RTX 2080Ti. 3. Results 3.1. Experimental comparison To confirm the efficacy of the proposed GCN method, we compared it with other competing approaches, including SVM, logistic regression (LR), MLP, GCN (Kipf and Welling 2016), ...