ResNets and its variants play an important role in various fields of image recognition. This paper gives another variant of ResNets, a kind of cross-residual learning networks called C-ResNets, which has less computation and parameters than ResNets. C-ResNets increases the information interaction between modules by densifying jumpers and enriches the role of jumpers. In addition, some meticulous designs on jumpers and channels counts can further reduce the resource consumption of C-ResNets and increase its classification performance. In order to test the effectiveness of C-ResNets, we use the same hyperparameter settings as fine-tuned ResNets in the experiments. We test our C-ResNets on datasets MNIST, FashionMnist, CIFAR-10, CIFAR-100, C...
Over the past few years, deep convolutional neural network (DCNN) based approaches have been immense...
Deep residual networks have recently emerged as the state-of-the-art architecture in image classific...
We introduce a GP generalization of ResNets (including ResNets as a particular case). We show that R...
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Com...
Residual neural network (ResNet) is a Deep Learning model introduced by He et al. in 2015 to enhance...
Deep neural networks are used in many applications such as image classification, image recognition, ...
Convolutional Neural Networks are widely used to process spatial scenes, but their computational cos...
Residual Network (ResNet) has gained considerable amount of attention in recent years as it has not ...
Various powerful deep neural network architectures have made great contribution to the exciting succ...
We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the pr...
Deep Residual Networks have recently been shown to significantly improve the performance of neural n...
Deep Residual Networks (ResNets) have recently achieved state-of-the-art results on many challenging...
Image denoising is a thoroughly studied research problem in the areas of image processing and comput...
Residual neural networks are widely used in computer vision tasks. They enable the construction of d...
Over-parameterized residual networks (ResNets) are amongst the most successful convolutional neural ...
Over the past few years, deep convolutional neural network (DCNN) based approaches have been immense...
Deep residual networks have recently emerged as the state-of-the-art architecture in image classific...
We introduce a GP generalization of ResNets (including ResNets as a particular case). We show that R...
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Com...
Residual neural network (ResNet) is a Deep Learning model introduced by He et al. in 2015 to enhance...
Deep neural networks are used in many applications such as image classification, image recognition, ...
Convolutional Neural Networks are widely used to process spatial scenes, but their computational cos...
Residual Network (ResNet) has gained considerable amount of attention in recent years as it has not ...
Various powerful deep neural network architectures have made great contribution to the exciting succ...
We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the pr...
Deep Residual Networks have recently been shown to significantly improve the performance of neural n...
Deep Residual Networks (ResNets) have recently achieved state-of-the-art results on many challenging...
Image denoising is a thoroughly studied research problem in the areas of image processing and comput...
Residual neural networks are widely used in computer vision tasks. They enable the construction of d...
Over-parameterized residual networks (ResNets) are amongst the most successful convolutional neural ...
Over the past few years, deep convolutional neural network (DCNN) based approaches have been immense...
Deep residual networks have recently emerged as the state-of-the-art architecture in image classific...
We introduce a GP generalization of ResNets (including ResNets as a particular case). We show that R...
Add to ORKG