Projective Latent Interventions for Understanding and Fine-Tuning Classifiers-Reference-Cited by-同舟云学术

Projective Latent Interventions for Understanding and Fine-Tuning Classifiers

Published:2020 Issue: Volume: Page:13-22
ISSN:0302-9743
Container-title:Interpretable and Annotation-Efficient Learning for Medical Image Computing
language:
Short-container-title:

Author:

Hinterreiter Andreas,Streit Marc,Kainz Bernhard

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-61166-8_2

Reference22 articles.

1. Baumgartner, C.F., et al.: SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans. Med. Imaging 36(11), 2204–2215 (2017). https://doi.org/10.1109/TMI.2017.2712367

2. Bellet, A., Habrard, A., Sebban, M.: A survey on metric learning for feature vectors and structured data. arXiv preprint arXiv:1306.6709 (2013)

3. Brent, R.P.: Algorithms for minimization without derivatives. Courier Corporation (2013)

4. Chen, X., Weng, J., Lu, W., Xu, J., Weng, J.: Deep manifold learning combined with convolutional neural networks for action recognition. IEEE Trans. Neural Netw. Learn. Syst. 29(9), 3938–3952 (2017). https://doi.org/10.1109/TNNLS.2017.2740318

5. Dong, W., Moses, C., Li, K.: Efficient k-nearest neighbor graph construction for generic similarity measures. In: Proceedings of the 20th International Conference on World Wide Web, pp. 577–586 (2011). https://www.cs.princeton.edu/cass/papers/www11.pdf

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. SpaceEditing: A Latent Space Editing Interface for Integrating Human Knowledge into Deep Neural Networks;Proceedings of the 29th International Conference on Intelligent User Interfaces;2024-03-18

2. Automatic Embedding Interventions for the Classification of Hematopoietic Cells;2022 26th International Conference on Pattern Recognition (ICPR);2022-08-21

3. Transparency of deep neural networks for medical image analysis: A review of interpretability methods;Computers in Biology and Medicine;2022-01