A. Viergever, "Quantitative evaluation of convolution-based methods for medical image interpolation," Medical Image Analysis, vol. 5, no. 2, pp. 111-126, 2001.E. H. W. Meijering, W. J. Niessen, and M. A. Viergever, "Quantitative evaluation of convolution-based methods for medical image...
一、前言神经网络大家都有所了解,CNN RNN LSTM transformer等。 如果不太了解,可以阅读神经网络模型相关文章: 笑个不停:sequence model-序列模型-RNN-GRU-LSTM(吴恩达课程学习笔记)笑个不停:【学习笔记】-…
correspondence model. The small differences between results with the three different constitutive models are explained. Users are provided with a step-by-step description of the problem setup and execution of the code. PeriFast/Dynamics is a branch of the PeriFast suite of codes, and is available...
However, these methods are vulnerable to adversarial training (AT) and/or are computationally heavy. In this work, we pro- pose a novel, model-free, Convolution-based Unlearnable DAtaset (CUDA) generation technique. CUDA is generated using controlled class-wise convolutions with filters that are...
Extensive experiments on various medical image segmentation tasks verify that our approach performs superior or on par with most contemporary works on an amalgamation of Vision Transformer and CNN-based methods, along with a significant reduction of model complexity. The codes and trained models are ...
We also compared HetConv convo- lution based model with the FLOPs compression methods and shown that it produces far better results as compared to compression methods. In the future, using this type of convolution, we can design more efficient architectures. Acknowledgmen...
(ViG) and CNN hybrid models have also been proposed for mobile vision tasks. However, all of these methods remain slower compared to pure CNN-based models. In this work, we propose Multi-Level Dilated Convolutions to devise a purely CNN-based mobile backbone. Using Multi-Level Dilated ...
In the RUL estimation, many deep learning methods Transferable cross-domain adaptation approach In this paper, a new transferable cross-domain adaptation method for predicting RUL is developed. The detailed flowchart of the model learning process is shown in Fig. 2. In the first stage, source ...
The results show that our method achieves state-of-the-art performance compared with the other five methods.doi:10.1007/s00521-018-03971-3Dong, ShizhouGao, ZhifanPirbhulal, SandeepBian, Gui-BinZhang, HeyeWu, WanqingLi, ShuoSpringer-VerlagNeural Computing and Applications...
some methods explore the semantics of category to constrain the common semantic space learning, such as semi-supervised methods [20,25] and supervised methods [18,21]. With the advance of deep learning in multimedia applications, deep neural networks (DNN) are used in cross-modal information ret...