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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Review of Deep Learning Algorithms for Image Semantic Segmentation
Published:
In this blog post, architecture of a few previous state-of-the-art models on image semantic segmentation challenges are detailed. Note that researchers test their algorithms using different datasets (PASCAL VOC, PASCAL Context, COCO, Cityscapes) which are different between the years and use different metrics of evaluation. Thus the cited performances cannot be directly compared per se.
Deep Learning Model Compression for Image Analysis: Methods and Architectures
Published:
This blog post describes theoretical methods to reduce model size. Size reduction for deep learning models is an active field of research. Those methods are truly performant, but the specific type of machine learning models used involves extremely deep and complex architectures (Simonyan et al. (2014), He et al. (2015), Szegedy et al. (2016)). How can we simply transform a deep model into a lighter one without decreasing drastically its performances ? Moreover, does it exist specialized architectures to build light models while achieving state-of-the-art performances ? Note that researchers test their algorithms using different datasets. Thus the cited accuracies cannot be directly compared per se.
Review of Deep Learning Algorithms for Object Detection
Published:
In this blog post I will review the state-of-the art of object detection models. I will provide details about the evolution of the architectures of the most accurate object detection models from 2012 up to today. One of my analysis criteria will be on their speed at inference allowing real-time analysis. Note that researchers test their algorithms using different datasets (PASCAL VOC, COCO, ImageNet) which are different between the years. Thus the cited accuracies cannot be directly compared per se.
Review of Deep Learning Algorithms for Image Classification
Published:
The purpose of this post is to provide a review of the state-of-the-art of image classification algorithms based on the most popular labelled dataset, ImageNet. We will describe some of the innovative architectures which lead to significant improvements. Note that researchers test their algorithms using different datasets (a new ImageNet dataset is released as a new challenge with different images each year). Thus the cited accuracies cannot be directly compared per se.
codeanddata
portfolio
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publications
talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Machine Learning
CES Data Science, Télécom Paris, 2019
Teaching assistant
- Clustering algorithms
Student Projects
2A and 3A, Télécom Paris, 2020
Co-supervisor of research student projects:
- SAR image denoising using SoTA method FFDNet.
- Temporal radar semantic segmentation.
Introduction to Computer Vision
Master 2 Data Science (Ecole Polytechnique), Télécom Paris, 2020
Teaching assistant
- Introduction to image processing
- Introduction to computer vision
Deep Learning I
Master 2 Data Science (Ecole Polytechnique), Télécom Paris, 2020
Teaching assistant for 2020 and 2021
- Multi-Layer-Perceptron (Numpy, Pytorch)
- Recurrent Neural Networks (Keras)
- Convolutional Neural Networks (Keras, Tensorflow)
Student Projects
1A, 2A and 3A, Télécom Paris, 2021
Co-supervisor of research student projects:
- Exploring loss functions for multi-view radar semantic segmentation.
- Exploring radar tensor aggregation methods for multi-view radar semantic segmentation.
- Iceberg monitoring using optical and SAR remote sensing data.
- Deforestation detection using optical and SAR remote sensing data.