SIDU-TXT: An XAI Algorithm for NLP with a Holistic Assessment Approach

no code implementations • 5 Feb 2024 • Mohammad N. S. Jahromi, Satya. M. Muddamsetty, Asta Sofie Stage Jarlner, Anna Murphy Høgenhaug, Thomas Gammeltoft-Hansen, Thomas B. Moeslund

Explainable AI (XAI) aids in deciphering 'black-box' models.

Sentiment Analysis

Learning-based Monocular 3D Reconstruction of Birds: A Contemporary Survey

no code implementations • 10 Jul 2022 • Seyed Mojtaba Marvasti-Zadeh, Mohammad N. S. Jahromi, Javad Khaghani, Devin Goodsman, Nilanjan Ray, Nadir Erbilgin

In nature, the collective behavior of animals, such as flying birds is dominated by the interactions between individuals of the same species.

3D Reconstruction Image to 3D +1

Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method

1 code implementation • 26 Jan 2021 • Satya M. Muddamsetty, Mohammad N. S. Jahromi, Andreea E. Ciontos, Laura M. Fenoy, Thomas B. Moeslund

Explainable Artificial Intelligence (XAI) has in recent years become a well-suited framework to generate human understandable explanations of "black-box" models.

Adversarial Attack Explainable artificial intelligence +1

SIDU: Similarity Difference and Uniqueness Method for Explainable AI

2 code implementations • 4 Jun 2020 • Satya M. Muddamsetty, Mohammad N. S. Jahromi, Thomas B. Moeslund

A new brand of technical artificial intelligence ( Explainable AI ) research has focused on trying to open up the 'black box' and provide some explainability.