Réunion

Alexandre Heuillet, AI/Computer Vision Scientist at Stereolabs

https://cv.hal.science/alexandre-heuillet

Titre: Differentiable Neural Architecture Search: a path to improve embedded machine vision efficiency

Abstract: The rapid proliferation of computer vision in edge devices—ranging from autonomous drones to wearable medical sensors—has created a critical tension between model accuracy and hardware constraints (especially regarding latency). While deep learning has revolutionized computer vision, the manual design of high-performance architectures is labor-intensive, intuition-driven without the certainty of an optimal solution and often results in over-parameterized models that exceed the power, thermal, and latency budgets of embedded systems.

This keynote explores Differentiable Neural Architecture Search (DNAS) as a transformative solution to this bottleneck. Unlike traditional NAS methods that rely on computationally expensive reinforcement learning or evolutionary algorithms, DNAS formulates the architecture selection process as a continuous optimization problem. By relaxing the discrete search space, we can leverage gradient-based optimization to simultaneously learn both network weights and the underlying structure yielding optimized architectures in only a few hours and allows for rapid prototyping.

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Jovita Lukasik, assistant professor, Visual Computing group, University of Siegen, Germanyhttps://jovitalukasik.github.io/

Title: Topology Learning for Multi-Objectiveness in Computer Vision

Abstract: Neural architecture search has emerged as a solution to the time-consuming trial-and-error process of manually designing architectures. Current research focuses on reducing the need for costly evaluations while efficiently exploring large search spaces through speed-up techniques, often using surrogate models for performance prediction. One such technique uses zero-cost proxies (ZCPs) to further increase evaluation efficiency. In this talk, I will present the potential of ZCPs in a multi-objective setting, with the goal of predicting not only the clean but also the robust accuracy of a neural network. In the second part of this talk, the implicit bias of classification networks will be investigated. These networks tend to prioritize high-frequency information, while adversarial training shifts the focus to low-frequency details. I will investigate the potential of steering the implicit bias of networks towards favoring low-frequency information to improve decision making based on these low-frequency information, such as shapes, which ultimately aligns more closely with human vision.

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Dominique Houzet, Professeur, Grenoble INP, Gipsa-lab

Title: Efficient Embedded Implementation of TDA for Deep Neural Networks

Abstract: La TDA fournit des descripteurs structurels compacts qui améliorent l'interprétabilité et la robustesse des réseaux profonds, notamment les CNNs, GNNs et Transformers. Les implémentations reposent sur des complexes simpliciaux ou cubiques, ou sur des approximations géométriques rapides via l'étiquetage de composantes connexes (CCL), permettant l'extraction efficace de signatures H₀ et H₁ depuis des données 2D et 3D.

Ces signatures topologiques constituent une forme de compression structurelle : elles réduisent des entrées de haute dimension à un petit ensemble d'invariants significatifs. Cette propriété a été proposée pour guider l'élagage de réseaux — en identifiant des filtres ou couches redondants — et pour améliorer la résilience adversariale en détectant les perturbations qui altèrent la topologie.

En exploitant le parallélisme GPU (CUDA, OpenMP), les pipelines TDA modernes peuvent atteindre des performances temps réel, ce qui les rend exploitables sur des systèmes embarqués ou à ressources limitées. Cette efficacité ouvre des perspectives pour la recherche d'architectures neuronales (NAS) : la compression topologique pourrait élargir l'espace de conception explorable, mais ce lien reste à ce stade une direction de recherche prospective.