Reference implementation for Blueprint Separable Convolutions (CVPR 2020)

This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.

Keras w/ Tensorflow backend implementation for 3D channel-wise convolutions

Sound event detection with depthwise separable and dilated convolutions.

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".

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.

Efficient Deep Learning for Real-time Classification of Astronomical Transients and Multivariate Time-series

Code for "Complex-Valued Depthwise Separable Convolutional Neural Network for Automatic Modulation Classification"

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.

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

Implementation of state-of-the-art models to do segmentation over our own dataset.

I Implemented some of the custom complex Convolutional Neural Network architecture using tensorow.keras Functional API.

Project crafted by Antonio Ferrigno, Giulia Di Fede and Vittorio Di Giorgio for the Advanced Machine Learning course at Politecnico di Torino (2023/2024)

Smart Automation Controller for Precision Agriculture

Neural Network for Low Complexity Acoustic Scene Classification