There are 2 repositories under depthwise-separable-convolutions topic.
Reference implementation for Blueprint Separable Convolutions (CVPR 2020)
Keras w/ Tensorflow backend implementation for 3D channel-wise convolutions
Online learning platform with automatic engagement recognition
Cheng-Hao Tu, Jia-Hong Lee, Yi-Ming Chan and Chu-Song Chen, "Pruning Depthwise Separable Convolutions for MobileNet Compression," International Joint Conference on Neural Networks, IJCNN 2020, July 2020.
This code implements the EEG Net deep learning model using PyTorch. The EEG Net model is based on the research paper titled "EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-Computer Interfaces".
Xception V1 model in Tensorflow with pretrained weights on ImageNet
PyTorch implementation of Depthwise Separable Convolution
MobileNet V2 transfer learning with TensorFlow 2.
"Advanced Machine Learning" project @ Politecnico di Torino, a.y. 2021/2022.
Code for "Complex-Valued Depthwise Separable Convolutional Neural Network for Automatic Modulation Classification"
A novel architecture for enhancing image classification. Reference paper: https://arxiv.org/abs/2104.12294
A TensorFlow2.0 implementation of Xception Deep Learning with Depthwise Separable Convolutions
This is the offcial implementation of ConvUNET for IEEE BIBM 2023.
I Implemented some of the custom complex Convolutional Neural Network architecture using tensorow.keras Functional API.
Smart Automation Controller for Precision Agriculture
Neural Network for Low Complexity Acoustic Scene Classification
Project crafted by Antonio Ferrigno, Giulia Di Fede and Vittorio Di Giorgio for the Advanced Machine Learning course at Politecnico di Torino (2023/2024)
Performance Evaluation between Normal and Depthwise Seperable Convolutions for Medical Image Classification.
This repository contains research on real-time domain adaptation in semantic segmentation, aiming at bridging the gap between synthetic and real-world imagery for urban scenes and autonomous driving, utilizing STDC models and advanced domain adaptation methods.
Implementation and research paper of MobileNet
Compare regular CNN with depthwise separable CNN for lightweight network
Lightweight Attention U-Net for Breast Cancer Semantic Segmentation