The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability
<|reference_start|>The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability: Interpretability techniques are valuable for helping humans understand and oversee AI systems. The SaTML 2024 CNN Interpretability Competition solicited novel methods for studying convolutional neural networks (CNNs) at the ImageNet scale. The objective of the competition was to help human crowd-workers identify trojans in CNNs. This report showcases the methods and results of four featured competition entries. It remains challenging to help humans reliably diagnose trojans via interpretability tools. However, the competition's entries have contributed new techniques and set a new record on the benchmark from Casper et al., 2023.<|reference_end|>
@article{casper2024the,
    title={The SaTML '24 CNN Interpretability Competition: New Innovations for
  Concept-Level Interpretability},
    author={Stephen Casper, Jieun Yun, Joonhyuk Baek, Yeseong Jung, Minhwan Kim,
  Kiwan Kwon, Saerom Park, Hayden Moore, David Shriver, Marissa Connor, Keltin
  Grimes, Angus Nicolson, Arush Tagade, Jessica Rumbelow, Hieu Minh Nguyen,
  Dylan Hadfield-Menell},
    journal={arXiv preprint arXiv:2404.02949},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02949},
    primaryClass={cs.LG cs.AI}
}