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Found 8 papers, 2 papers with code
Enhancing Post-Hoc Explanation Benchmark Reliability for Image Classification
Deep neural networks, while powerful for image classification, often operate as "black boxes," complicating the understanding of their decision-making processes.
Handwritten Text Recognition from Crowdsourced Annotations
However, selecting training samples based on the degree of agreement between annotators introduces a bias in the training data and does not improve the results.
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Handwritten Text Recognition
on Belfort
Model-based inexact graph matching on top of CNNs for semantic scene understanding
On FASSEG data, results show that our module improves accuracy of the CNN by about 6. 3% (the Hausdorff distance decreases from 22. 11 to 20. 71).
Comparison of attention models and post-hoc explanation methods for embryo stage identification: a case study
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 deep learning architectures, which raises potential bias and fairness issues.
Towards deep learning-powered IVF: A large public benchmark for morphokinetic parameter prediction
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 and evaluate deep learning (DL) models.
Metrics for saliency map evaluation of deep learning explanation methods
Due to the black-box nature of deep learning models, there is a recent development of solutions for visual explanations of CNNs.
BR-NPA: A Non-Parametric High-Resolution Attention Model to improve the Interpretability of Attention
The prevalence of employing attention mechanisms has brought along concerns on the interpretability of attention distributions.
A General Framework for the Recognition of Online Handwritten Graphics
We then model the recognition problem as a graph parsing problem: given an input stroke set, we search for a parse tree that represents the best interpretation of the input.
