Raphaël Achddou

Télécom Paris, 19 Place Marguerite Perey, 91120 Palaiseau raphael dot achddou at telecom-paris dot fr

1st year PHD student at Télécom Paris, Institut Polytechnique de Paris. My research interests are image restoration, deep learning, and computer vision.


Publications

Nested Learning for Multi-Granular Tasks

R. Achddou, J.Matias di Martino, Guillermo Sapiro

Standard deep neural networks (DNNs) are commonly trained in an end-to-end fashion for specific tasks such as object recognition, face identification, or character recognition, among many examples. This specificity often leads to overconfident models that generalize poorly to samples that are not from the original training distribution. Moreover, such standard DNNs do not allow to leverage information from heterogeneously annotated training data, where for example, labels may be provided with different levels of granularity. To address these challenges, we introduce the concept of nested learning: how to obtain a hierarchical representation of the input such that a coarse label can be extracted first, and sequentially refine this representation, if the sample permits, to obtain successively refined predictions, all of them with the corresponding confidence.


Experience

Research Intern

Duke ECE - Sapiro Lab

6 months Master internship under the supervision of Pr. Guillermo Sapiro and Pr. Matias Di Martino. During this internship, we jointly worked on a theoretical deep learning problem. The goal was to propose a novel way of tackling classification problems, by enforcing nested predictions. Most classification tasks can indeed be split in a nested taxonomy, which we exploit by providing multi-granular outputs, with the appropriate confidence. This projected resulted in a paper which shoud be available soon.

April 2019 - October 2019

Education

Ecole Normale Supérieure Paris-Saclay

Master M.V.A.
M2 (Msc) in computer vision, machine learning theory and practice.

Courses followed : Image Denoising, Deep Learning, Mathematical foundations of Deep Learning, Mathematics of Imaging, Deep Leartning for Image Restoration, Online Learning, Reinforcement Learning, Object Recognition, Convex Optimization, Medical Imaging

October 2018 - October 2019

Télécom Paristech

Engineering Degree

Engineering degree in Telecommunications. The main topics studied are signal processing, computer science, and maths.

September 2016 - October 2019

Interests

Apart from academical works, I really enjoy photography, especially when I travel. I mostly do street photography in Paris, and sometimes in Berlin, New York, Rome, etc. I shall release a gallery of those pictures soon.

Other than that, I always enjoy to take a dip in the over-crowded Parisian swimming pools, since I "retired" from my almost non-existing swimming career.