This repo contains all of the codes of my theoretical thesis in MSc supervised by Dr. Amin Gheibi, conducted at Amirkabir University of Technology (Tehran Polytechnic).
- We theoretically connected the Lipschitz constant and maximum value of a loss function to the generalization error of deep learning models trained by the Adam and AdamW optimizers under the uniform stability theory.
- Using the theoretical results, we proposed a novel loss function for training deep classification models to improve the generalization performance and overcome the over-fitting issue.
- We assessed our theorems in human age estimation based on face images.
- We trained deep neural networks using our new loss function in the image and node classification problems in order to stabilize the output models and increase their accuracy.
- Requirements:
- Python 3.7.8
- SciPy 1.7.3
- OpenCV 4.5.5
- PyTorch 1.11
- CUDA 11.3
- Torchvision 0.12
- PyTorch Geometric 2.1
- An original paper containing the theorems, proofs, and experiments on age estimation has been accepted for publication in Amirkabir Journal of Mathematics and Computing (AJMC);
- The article on the journal website: https://ajmc.aut.ac.ir/article_5213.html;
- PDF on arXiv: https://arxiv.org/abs/2303.16464.