Moritz Böhle

IEEE Trans. Pattern Anal. Mach. Intell., June, 2024

B-Cos Alignment for Inherently Interpretable CNNs and Vision Transformers.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., June, 2024

Towards designing inherently interpretable deep neural networks for image classification.

[BibT_eX]

[DOI]

PhD thesis, 2024

Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery.

[BibT_eX]

[DOI]

CoRR, 2024

Good Teachers Explain: Explanation-Enhanced Knowledge Distillation.

[BibT_eX]

[DOI]

CoRR, 2024

2023

Optimising for Interpretability: Convolutional Dynamic Alignment Networks.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., June, 2023

Holistically Explainable Vision Transformers.

[BibT_eX]

[DOI]

CoRR, 2023

Temperature Schedules for self-supervised contrastive methods on long-tail data.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Studying How to Efficiently and Effectively Guide Models with Explanations.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Towards Better Understanding Attribution Methods.

[BibT_eX]

[DOI]

Sukrut Rao

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

B-cos Networks: Alignment is All We Need for Interpretability.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Convolutional Dynamic Alignment Networks for Interpretable Classifications.

[BibT_eX]

[DOI]