1 code implementation • 21 Feb 2024 • Michael Arbel, Alexandre Zouaoui
Replicability in machine learning (ML) research is increasingly concerning due to the utilization of complex non-deterministic algorithms and the dependence on numerous hyper-parameter choices, such as model architecture and training datasets.
1 code implementation • 23 Jan 2024 • Behnood Rasti, Alexandre Zouaoui, Julien Mairal, Jocelyn Chanussot
Our experimental results validate that enforcing the convexity constraint outperforms the sparsity prior for the endmember library.
1 code implementation • 18 Aug 2023 • Behnood Rasti, Alexandre Zouaoui, Julien Mairal, Jocelyn Chanussot
Additionally, we draw a critical comparison between advanced and conventional techniques from the three categories.
1 code implementation • 9 Aug 2023 • Behnood Rasti, Alexandre Zouaoui, Julien Mairal, Jocelyn Chanussot
Unlike most conventional sparse unmixing methods, here the minimization problem is non-convex.
1 code implementation • 22 Sep 2022 • Alexandre Zouaoui, Gedeon Muhawenayo, Behnood Rasti, Jocelyn Chanussot, Julien Mairal
In this paper, we introduce a new algorithm based on archetypal analysis for blind hyperspectral unmixing, assuming linear mixing of endmembers.
2 code implementations • NeurIPS 2021 • Théo Bodrito, Alexandre Zouaoui, Jocelyn Chanussot, Julien Mairal
Hyperspectral imaging offers new perspectives for diverse applications, ranging from the monitoring of the environment using airborne or satellite remote sensing, precision farming, food safety, planetary exploration, or astrophysics.