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On going


Conditions:The projects are reserved for EPFL students or students of mobility program.

Ongoing projects

All Project

Number of projects:5

Lipschitz Constrained Generative Adversarial Networks

Polina Proskura
Master Semester Project: Winter 2020

In generative adversarial networks, it has been shown that controlling the Lipschitz regularity of a network largely improves the generative performance. For example, Wasserstein GAN (WGAN) and Spectral Normalization GAN (SNGAN) achieve this by restricting the discriminative function to be 1-Lipschitz. Recently, we developed a framework for learning activations of deep neural networks with the motivation of controlling the global Lipschitz constant of the input-output relation. The goal of this project is to investigate the effect of our framework in the generative adversarial networks within various setups. The student should have solid programming skills, in particular being familiar with PyTorch and a general understanding of the main concepts of deep learning.

References Aziznejad, S., Gupta, H., Campos, J., & Unser, M. (2020). Deep neural networks with trainable activations and controlled Lipschitz constant. arXiv preprint arXiv:2001.06263.


Review and implementation of loss functions for image-to-image neural network

Alexandre Levy
Master Semester Project: Summer 2020

- Make a complete review of the loss function for image2image applications - Implement a large collection of loss function as a ImageJ plugin in the framework of deepImage loss function: SNR, MSE, cross-entropy, SSIM, Jaccard, Wasserstein …. - Write protocols to test different loss function to train u-net in Jupyter Notebooks - Application: image denoising, image segmentation


Dynamic of cracks in wall based on analysis of image sequence

Emma Bouton-Bessac
Master Semester Project: Winter 2020


Growth of E. coli cells: simulation of images data to training of a neural network (Prof. McKinney)

Héloïse Monnet
Master Semester Project: Winter 2020


Deep neural network for SIM super-resolution reconstruction with a reduced number of images

Kay Lächer
Master Semester Project: Winter 1998

In the standard 2D-SIM set-up, we need 9 images to reconstruct a super-resolution image. We want to reduce the number of images (to have fast acquisition + to limit the photo-toxicity + to make longer experiment) while keeping almost the same quality. Hence, we need to train a neural network to learn the missing information from the data.


2020 EPFL • webmaster.big@epfl.ch18.09.2020