MFPP: Morphological Fragmental Perturbation Pyramid for Black-Box Model Explanations
| Autor: | Xia Zhu, Jong-Kae Fwu, Ganmei You, Yuan Zhu, Qing Yang, Yun Ye |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Pixel Artificial neural network Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Pattern recognition 02 engineering and technology Image (mathematics) Fragment (logic) 020204 information systems Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Transparency (data compression) Pyramid (image processing) Artificial intelligence business Focus (optics) |
| Zdroj: | ICPR |
| Popis: | Deep neural networks (DNNs) have recently been applied and used in many advanced and diverse tasks, such as medical diagnosis, automatic driving, etc. Due to the lack of transparency of the deep models, DNNs are often criticized for their prediction that cannot be explainable by human. In this paper, we propose a novel Morphological Fragmental Perturbation Pyramid (MFPP) method to solve the Explainable AI problem. In particular, we focus on the black-box scheme, which can identify the input area that is responsible for the output of the DNN without having to understand the internal architecture of the DNN. In the MFPP method, we divide the input image into multi-scale fragments and randomly mask out fragments as perturbation to generate a saliency map, which indicates the significance of each pixel for the prediction result of the black box model. Compared with the existing input sampling perturbation method, the pyramid structure fragment has proved to be more effective. It can better explore the morphological information of the input image to match its semantic information, and does not need any value inside the DNN. We qualitatively and quantitatively prove that MFPP meets and exceeds the performance of state-of-the-art (SOTA) black-box interpretation method on multiple DNN models and datasets. Accepted by 25th International Conference on Pattern Recognition (ICPR2020) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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