6 citations · 10 across the 5 of their papers we have counts for
5 papers
Enhancing Post-Hoc Explanation Benchmark Reliability for Image Classification
Tristan Gomez, Harold Mouchère
Deep neural networks, while powerful for image classification, often operate as "black boxes," complicating the understanding of their decision-making processes. Various explanatio…
Comparison of attention models and post-hoc explanation methods for embryo stage identification: a case study
Tristan Gomez, Thomas Fréour, Harold Mouchère
An important limitation to the development of AI-based solutions for In Vitro Fertilization (IVF) is the black-box nature of most state-of-the-art models, due to the complexity of…
Towards deep learning-powered IVF: A large public benchmark for morphokinetic parameter prediction
Tristan Gomez, Magalie Feyeux, Nicolas Normand +4
An important limitation to the development of Artificial Intelligence (AI)-based solutions for In Vitro Fertilization (IVF) is the absence of a public reference benchmark to train…
Metrics for saliency map evaluation of deep learning explanation methods
Tristan Gomez, Thomas Fréour, Harold Mouchère
Due to the black-box nature of deep learning models, there is a recent development of solutions for visual explanations of CNNs. Given the high cost of user studies, metrics are ne…
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of Attention
Tristan Gomez, Suiyi Ling, Thomas Fréour +1
The prevalence of employing attention mechanisms has brought along concerns on the interpretability of attention distributions. Although it provides insights about how a model is o…