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Document type:
Masterarbeit
Author(s):
Maresa Schröder
Title:
Explanations from the Latent Space: The Need for Latent Feature Saliency Detection in Deep Time Series Classification
Abstract:
Deep Leaning models are widely used for time series classification. For understanding the decision-making process of the model and identifying artifacts, explainability methods for these black-box classifiers are necessary. State-of-the-art saliency methods, originally developed for image data, assign importance scores to image pixels, providing visual explainability by highlighting informative regions in images. These methods have also been utilized for time series classification, where they eq...     »
Subject:
MAT Mathematik
DDC:
510 Mathematik
Supervisor:
Mathias Drton
Advisor:
Narges Ahmidi, Oleksandr Zadorozhnyi, Alireza Zamanian
Referee:
Mathias Drton
Date of acceptation:
14.06.2022
Year:
2022
Quarter:
2. Quartal
Year / month:
2022-06
Month:
Jun
Pages:
94
Language:
en
University:
Technische Universität München
Faculty:
Fakultät für Mathematik
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
 BibTeX