Abstract
In recent years, many deep neural architectures have been developed for image classification. Whether they are similar or dissimilar and what factors contribute to their (dis)similarities remains curious. To address this question, we aim to design a quantitative and scalable similarity measure between neural architectures. We propose Similarity by Attack Transferability (SAT) from the observation that adversarial attack transferability contains information related to input gradients and decision boundaries widely used to understand model behaviors. We conduct a large-scale analysis on 69 state-of-the-art ImageNet classifiers using our SAT to answer the question. In addition, we provide interesting insights into ML applications using multiple models, such as model ensemble and knowledge distillation. Our results show that using diverse neural architectures with distinct components can benefit such scenarios.
Original language | English |
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Title of host publication | Computer Vision – ECCV 2024 - 18th European Conference, Proceedings |
Editors | Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 106-126 |
Number of pages | 21 |
ISBN (Print) | 9783031731129 |
DOIs | |
Publication status | Published - 2025 |
Event | 18th European Conference on Computer Vision, ECCV 2024 - Milan, Italy Duration: 2024 Sept 29 → 2024 Oct 4 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 15126 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 18th European Conference on Computer Vision, ECCV 2024 |
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Country/Territory | Italy |
City | Milan |
Period | 24/9/29 → 24/10/4 |
Bibliographical note
Publisher Copyright:© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- General Computer Science