ResidualAttentionNetwork
Attention network
A Gluon implementation of Residual Attention Network for image classification tasks
A Gluon implement of Residual Attention Network. Best acc on cifar10-97.78%.
108 stars
6 watching
26 forks
Language: Python
last commit: over 5 years ago
Linked from 1 awesome list
cifar10deep-learninggluongluon-cvmxnetresidual-attention-networksota
Related projects:
Repository | Description | Stars |
---|---|---|
| An image classification neural network implementation using attention mechanisms and residual learning | 94 |
| An implementation of a deep neural network architecture using attention mechanisms and residual connections for image classification tasks. | 554 |
| An MXNet implementation of a modified deep neural network architecture for image classification | 67 |
| Implementing a deep learning framework for image classification using Residual Attention Network architecture | 682 |
| An implementation of a neural network architecture for document classification using hierarchical attention mechanisms | 87 |
| A deep learning framework for semantic segmentation with spatial attention mechanisms | 218 |
| A Torch implementation of a novel neural network architecture designed to improve the generalization ability of deep image classification models. | 129 |
| Develops a single convolutional network to handle various image degradations with improved scalability and efficiency | 427 |
| An implementation of Wide Residual Networks in PyTorch for efficient deep learning on CIFAR10/100 datasets. | 334 |
| An open-source implementation of dilated residual networks for image classification and segmentation tasks. | 1,104 |
| An optimized neural network architecture for image classification tasks by combining SqueezeNet with residual connections. | 155 |
| A PyTorch implementation of a graph neural network model that learns personalized node representations | 367 |
| A lightweight neural network library for training and prediction tasks | 2,112 |
| Software for enhancing satellite images through deep learning techniques | 76 |
| An experimental study on residual networks to improve depth and width trade-offs in neural networks | 1,299 |