There are 4 repositories under dilated-convolution topic.
Image denoising using deep CNN with batch renormalization(Neural Networks,2020)
This is an ongoing re-implementation of DeepLab_v3_plus on pytorch which is trained on VOC2012 and use ResNet101 for backbone.
GANs for Time series analysis (Synthetic data generation, anomaly detection and interpolation), Hypertuning using Optuna, MLFlow and Databricks
Enhanced CNN for image denoising (CAAI Transactions on Intelligence Technology, 2019)
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment [CVPR 2019]
[SAIN'18] [Caffe] A dilated version of FCN with Stride 2 for Efficient Semantic Segmentation
Classifying audio using Wavelet transform and deep learning
An implementation of dilated convolutional layer based on Darknet Architecture
Succeeded by SyntaxDot: https://github.com/tensordot/syntaxdot
A Numpy implementation of the dilated/atrous CNNs proposed by Yu et al. as well as transposed convolutions.
[preprint] AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation
Classify bird species based on their songs using SIamese Networks and 1D dilated convolutions.
An implementation of DetNet with Keras.
Chapter 6: Convolutional Neural Networks
comprehensive collection of powerful techniques for time series data visualization, analysis and modeling
PyTorch implementation of Dilated Residual Networks for semantic image segmentation
Hybrid Data Augmentation and Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition
Dilation Rate Gridding Problem and How to Solve It With the Fibonacci Sequence.
Time Series Forecasting Best Practices & Examples
Implementation of DeSnowNet paper - https://arxiv.org/pdf/1708.04512.pdf
Neural Network for Low Complexity Acoustic Scene Classification
Advance Convolutions, Attention and Image Augmentation: Depth wise, Pixel Shuffle, Dilated, Transpose, Channel Attention, and Albumentations Library
Dilated Convolutional Autoencoder for univariate Time Series
Program implements a convolutional neural network for classifying images of numbers in the MNIST dataset as either even or odd using GPU framework.
This is an implementation of the "Fast Image Processing with Fully-Convolutional Networks" paper.