dropblock
Convolutional regularizer
Regularizes convolutional networks by randomly dropping units in contiguous regions of feature maps
Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.
589 stars
9 watching
95 forks
Language: Python
last commit: over 4 years ago computer-visionconvolutional-neural-networksdropblockdropoutmachine-learningpytorchpytorch-implementationregularization
Related projects:
Repository | Description | Stars |
---|---|---|
| This project explores training deep neural networks using noisy labels with dropout regularization to improve robustness. | 11 |
| Implementations of Dilated Recurrent Neural Networks in PyTorch | 211 |
| A PyTorch implementation of an attention network for dynamic scene deblurring | 37 |
| PyTorch implementation of the CBAM module for refining feature maps in deep networks | 1,353 |
| An implementation of Filter Response Normalization Layer in PyTorch to improve the training of deep neural networks by eliminating batch dependence. | 86 |
| An implementation of a 1-bit weight neural network architecture using PyTorch | 124 |
| An implementation of a regularization technique to improve the accuracy of deep learning models trained with noisy labels. | 46 |
| Implementation of an algorithm for single image deblurring in images with defocus blur | 228 |
| A PyTorch implementation of compact bilinear pooling, an efficient downsampling technique used in computer vision and other image processing applications. | 183 |
| An implementation of Deformable Convolution in PyTorch using CUDA. | 409 |
| This project provides a PyTorch implementation of pruning techniques to reduce the computational resources required for neural network inference. | 877 |
| A Python implementation of a Convolutional Neural Network from scratch using NumPy for building CNNs from scratch | 577 |
| Implementing a deep learning framework for image classification using Residual Attention Network architecture | 682 |
| An implementation of a lightweight convolutional neural network architecture for mobile devices | 191 |
| Develops a deep learning model for single image deblurring with improved performance and computational efficiency | 382 |