RDFIA: Reconnaissance Des Formes par Intelligence Artificielle (Recognition and Description of Patterns with Artificial Intelligence)
This repository contains practical works (PW) I completed (with Nathan Galmiche) as part of my Master's course in deep learning for visual understanding (https://cord.isir.upmc.fr/teaching-rdfia/).
You can take a look at the reports we wrote by clicking to this link: https://github.com/pictoune/RDFIA/blob/main/all_reports.pdf.
- PW 1: SIFT & BOW, SVM
- PW 2: Transfer Learning & Visualization, Domain Adaptation & GAN
- PW 3: Advanced Topics in Transfer Learning and GANs
- PW 4: Bayesian Methods and Uncertainty in Deep Learning
This section contains Python code (Jupyter notebook) for image processing with SIFT & BOW and SVM techniques, and a report presenting the results.
- Code: SIFT & BOW, SVM
- Report: PDF
This section contains Python code (Jupyter notebook) for exploring transfer learning, visualization, domain adaptation, and GANs, and a report presenting the results.
- Code: Transfer Learning & Visualization, Domain Adaptation & GAN
- Report: PDF
This section contains Python code (Jupyter notebook) for advanced topics in transfer learning and GANs, and a report presenting the results.
- Code: Transfer Learning, Visualizing Neural Networks, Domain Adaptation, GANs
- Report: PDF
This section contains Python code (Jupyter notebook) for Bayesian methods and uncertainty in deep learning, and a report presenting the results.
- Code: Bayesian Linear Regression, Approximate Inference, Uncertainty Applications
- Report: PDF
This project is open source and available under the MIT License.
Feel free to explore the projects and reach out if you have any questions or suggestions.