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Estimating the Robustness of Classification Models by the Structure of the Learned Feature-Space

  • Over the last decade, the development of deep image classification networks has mostly been driven by the search for the best performance in terms of classification accuracy on standardized benchmarks like ImageNet. More recently, this focus has been expanded by the notion of model robustness, \ie the generalization abilities of models towards previously unseen changes in the data distribution.Over the last decade, the development of deep image classification networks has mostly been driven by the search for the best performance in terms of classification accuracy on standardized benchmarks like ImageNet. More recently, this focus has been expanded by the notion of model robustness, \ie the generalization abilities of models towards previously unseen changes in the data distribution. While new benchmarks, like ImageNet-C, have been introduced to measure robustness properties, we argue that fixed testsets are only able to capture a small portion of possible data variations and are thus limited and prone to generate new overfitted solutions. To overcome these drawbacks, we suggest to estimate the robustness of a model directly from the structure of its learned feature-space. We introduce robustness indicators which are obtained via unsupervised clustering of latent representations from a trained classifier and show very high correlations to the model performance on corrupted test data.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/6447
Bibliografische Angaben
Title (English):Estimating the Robustness of Classification Models by the Structure of the Learned Feature-Space
Conference:The AAAI-22 Workshop on Adversarial Machine Learning and Beyond (AAAI-22 AdvML Workshop), Feb 28 2022, Vancouver, BC, Canada
Author:Kalun Ho, Franz-Josef Pfreundt, Janis KeuperStaff MemberORCiDGND, Margret Keuper
Year of Publication:2022
Creating Corporation:Association for the Advancement of Artificial Intelligence
First Page:1
Last Page:10
Parent Title (English):The AAAI-22 Workshop on Adversarial Machine Learning and Beyond
URL:https://openreview.net/forum?id=UHBsuFPrJ11
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Forschung / IMLA - Institute for Machine Learning and Analytics
Institutes:Bibliografie
Tag:Machine Learning
Formale Angaben
Relevance:Konferenzbeitrag: h5-Index < 30
Open Access: Open Access 
 Bronze 
Licence (German):License LogoUrheberrechtlich geschützt