Applying Explanatory Methods on Convolutional Neural Networks (CROSBI ID 451508)
Ocjenski rad | diplomski rad
Podaci o odgovornosti
Luke Frederick Walker
Pintar, Damir
engleski
Applying Explanatory Methods on Convolutional Neural Networks
Convolutional neural networks are known for being capable of increadible feats in computer vision, but as a black box model they suffer from a lack of transparency. There are explanatory methods that attempt to give insights into the inference methodology of these networks. These methods are highly subjective in nature, and there is no objective, universally agreed way to measure their usefulness. These methods are described in detail, with descriptions on the progress that has been made on their improvement over time. The methods are then used and compared on various datasets, with attention given to their applicability, insightfulness, and ease of use.
convolutional neural networks ; explanatory methods ; feature visualization ; network dissection ; pixel attribution
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Podaci o izdanju
50
05.07.2022.
obranjeno
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Fakultet elektrotehnike i računarstva
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