Towards Complementary Explanations Using Deep Neural Networks

By Wilson Silva, Kelwin Fernandes, M. Jorge Cardoso, Jaime S. Cardoso. 2018

50 citations recorded by the source corpus.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Christin Seifert
2. Meike Nauta
3. Amitojdeep Singh
4. Elisa Nguyen
5. Maurice van Keulen
6. Ron van Bree
7. Michelle Peters
8. Yasmin Schmitt

Citations in the corpus

• Neural Prototype Trees for Interpretable Fine-grained Image Recognition (2020, background): “Interpretability can be quantified by explanation size [11, 45].”
• From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2022, background): “It can reflect two facets of certainty: i) a confidence measure of the black box prediction [17, 32, 214, 289], or ii) the truthfulness or likelihood of the explanation [17, 166, 233].”
• Explainable Deep Learning Models in Medical Image Analysis (2020, background): “Another approach used monotonic constraints to explain the predictions in terms of style and depth two datasets - dermoscopy images and post-surgical breast aesthetics [63].”