Gradient-Based Attribution Methods

Author:

Ancona Marco,Ceolini Enea,Öztireli Cengiz,Gross Markus

Publisher

Springer International Publishing

Reference38 articles.

1. Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/

2. Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: Advances in Neural Information Processing Systems, pp. 9524–9535 (2018)

3. Ancona, M., Ceolini, E., Oztireli, C., Gross, M.: Towards better understanding of gradient-based attribution methods for deep neural networks. In: 6th International Conference on Learning Representations (ICLR) (2018)

4. Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7), e0130140 (2015)

5. Balduzzi, D., Frean, M., Leary, L., Lewis, J.P., Ma, K.W.D., McWilliams, B.: The shattered gradients problem: if resnets are the answer, then what is the question? In: Proceedings of the 34th International Conference on Machine Learning, ICML 2017, vol. 70, pp. 342–350 (2017). JMLR.org

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