wide-residual-networks
by szagoruyko
3.8% and 18.3% on CIFAR-10 and CIFAR-100
AI summary
ResNet experiment
An experimental study on residual networks to improve depth and width trade-offs in neural networks
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Similar projects
Found by comparing what the projects do, not just their names.
Neural network architecture
An implementation of a 1-bit weight neural network architecture using PyTorch
Deep network architecture
An implementation of Wide Residual Networks in PyTorch for efficient deep learning on CIFAR10/100 datasets.
Attention network
A Gluon implementation of Residual Attention Network for image classification tasks
Deep learning experiments
An experiment with various deep learning libraries and frameworks on images and time series data
ResNet implementation
An implementation of Residual Networks using Keras' functional API
Image classifier
PyTorch implementation of wide residual networks for image classification tasks on CIFAR-10 and CIFAR-100 datasets
cszn/srmd427
Image restoration network
Develops a single convolutional network to handle various image degradations with improved scalability and efficiency
WRN model
Keras implementation of Wide Residual Networks with preloaded weights and configuration options for training and testing
Attention Network
An implementation of a deep neural network architecture using attention mechanisms and residual connections for image classification tasks.
Image classifier
Implementing a deep learning framework for image classification using Residual Attention Network architecture
Attention transfer
Improves performance of convolutional neural networks by transferring knowledge from teacher models to student models using attention mechanisms.
Residual Network
An optimized neural network architecture for image classification tasks by combining SqueezeNet with residual connections.
Deep learning models
Implementations of deep learning architectures using PyTorch for image classification tasks on various datasets.
ResNet replication
An implementation of Residual Networks In Residual Networks using Chainer.
Neural network architecture
Represents a neural network architecture with 1K layers, designed for image recognition tasks.
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