Igeood: An Information Geometry Approach to Out-of-Distribution Detection

1 code implementation • ICLR 2022 • Eduardo Dadalto Camara Gomes, Florence Alberge, Pierre Duhamel, Pablo Piantanida

Reliable out-of-distribution (OOD) detection is fundamental to implementing safer modern machine learning (ML) systems.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Lossless Coding of Point Cloud Geometry using a Deep Generative Model

1 code implementation • 1 Jul 2021 • Dat Thanh Nguyen, Maurice Quach, Giuseppe Valenzise, Pierre Duhamel

This paper proposes a lossless point cloud (PC) geometry compression method that uses neural networks to estimate the probability distribution of voxel occupancy.

Data Augmentation

Multiscale deep context modeling for lossless point cloud geometry compression

2 code implementations • 20 Apr 2021 • Dat Thanh Nguyen, Maurice Quach, Giuseppe Valenzise, Pierre Duhamel

We propose a practical deep generative approach for lossless point cloud geometry compression, called MSVoxelDNN, and show that it significantly reduces the rate compared to the MPEG G-PCC codec.

Learning-based lossless compression of 3D point cloud geometry

1 code implementation • 30 Nov 2020 • Dat Thanh Nguyen, Maurice Quach, Giuseppe Valenzise, Pierre Duhamel

On the one hand, octree representation can eliminate the sparsity in the point cloud.

Learning Anonymized Representations with Adversarial Neural Networks

1 code implementation • 26 Feb 2018 • Clément Feutry, Pablo Piantanida, Yoshua Bengio, Pierre Duhamel

Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations.

Representation Learning Sentiment Analysis