Dans le monde de l'intelligence artificielle (IA), l'un des domaines les plus passionnants et
prometteurs est l'adaptation de domaine. Mais qu'est-ce que c'est exactement et
Comment cela pourrait-il être avantageux pour votre entreprise? Analysons cela en détail.
Qu'est-ce que l'adaptation de domaine ?
L'adaptation de domaine est une technique utilisée en apprentissage automatique où un
modèle prédictif, entraîné sur un domaine (ou source), est adapté pour être efficace sur un

In this subject, we investigate ways to accelerate local search-based algorithms for combinatorial optimization problems through reinforcement learning techniques.

TRAIL vise à créer une structure permettant de mobiliser les capacités de recherche e

Properly understanding the performances of classifiers is essential in various scenarios. However, the literature often relies only on one or two standard scores to compare classifiers, which fails to capture the nuances of application-specific requirements, potentially leading to suboptimal classifier selection. Recently, a paper on the foundations of the theory of performance-based ranking introduced a tool, called the Tile, that organizes an infinity of ranking scores into a 2D map.

In the computer vision and machine learning communities, as well as in many other research domains, rigorous evaluation of any new method, including classifiers, is essential. One key component of the evaluation process is the ability to compare and rank methods. However, ranking classifiers and accurately comparing their performances, especially when taking application-specific preferences into account, remains challenging. For instance, commonly used evaluation tools like Receiver Operating Characteristic (ROC) and Precision/Recall (PR) spaces display performances based on two scores.

Ranking entities such as algorithms, devices, methods, or models based on their performances, while accounting for application-specific preferences, is a challenge. To address this challenge, we establish the foundations of a universal theory for performance-based ranking. First, we introduce a rigorous framework built on top of both the probability and order theories.

Characterizing domains is essential for models analyzing dynamic environments, as it allows them to adapt to evolving conditions or to hand the task over to backup systems when facing conditions outside their operational domain. Existing solutions typically characterize a domain by solving a regression or classification problem, which limits their applicability as they only provide a limited summarized description of the domain. In this paper, we present a novel approach to domain characterization by characterizing domains as probability distributions.

Computer vision tasks such as object detection and segmentation rely on the availability of extensive, accurately annotated datasets. In this work, We present CIA, a modular pipeline, for (1) generating synthetic images for dataset augmentation using Stable Diffusion, (2) filtering out low quality samples using defined quality metrics, (3) forcing the existence of specific patterns in generated images using accurate prompting and ControlNet.