Debugging image classification algorithms using human assisted feedback

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Kadircan_Becek_Thesis.pdf

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2022-12-15

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Becek, Kadircan

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Neural Network debugging is a relatively newly emerged field of deep learning which tries to improve upon learned parameters with external assistance. Two academic fields are particularly active in addressing this issue: the field of feature visualiza- tions and explainable artificial intelligence. While these methods can improve how to interpret a neural network, they can not provide the expert opinion of a human to improve the accuracy of a neural network. Therefore, while being interpreted cor- rectly, these networks can have undesired outputs. For instance, they may have biases against some classes or may not work effectively in the wild due to overfitting. In this thesis, we propose a method with two steps to tackle this problem: (1) present- ing feature contribution using visualization, (2) taking feedback from humans, and retraining the network by disabling irrelevant or adversarial features.

Subject Keywords

Explainable AI, Feature visualization, Debugging

URI

https://hdl.handle.net/11511/101260

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

K. Becek, “Debugging image classification algorithms using human assisted feedback,” M.S. - Master of Science, Middle East Technical University, 2022.