Published March 31, 2023 | Version 1.0
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Report on the maturation of local characterisation tools

Description

The contributions in this deliverable are divided into 4 different parts. The first three correspond to the three tools matured in this action sheet, while the last one correspond to the evaluation of Chiru. A quick summary of the different tool and use cases tested is given below:


Chapter 2: AI Metamorphism Observing Software from CEA

Tested on: UC Renault Welding, UC Safran Visual Industrial Control

Short description: Assesses metamorphic properties on AI models such as robustness to perturbations on the inputs but also relation between models’ inputs and outputs.

 

Chapter 3: Amplification methods for robustness evaluation from LNE
Tested on: UC Valeo Scene understanding, UC Air Liquid Demand Forecasting, UC Safran Visual Industrial Control

Short description: Evaluates the robustness of models using amplification methods on the dataset with noise functions.

Chapter 4: Non-overlapping corruption benchmarking tool from Atos

Tested on: UC Atos ReID

Short description: Provides a benchmark of synthetic corruptions on a dataset to assess the robustness of a given AI-based model.

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Additional details

Trustworthy Attributes
Robustness
Use cases
Vision
Visual Inspection
Time series
NLP
Functional Set
Evaluation
Robustness
Model Component Life Cycle