Explaining Neural Networks by Decoding Layer Activations

Baldi, P., Hornik, K.: Neural networks and principal component analysis: Learning from examples without local minima. Neural networks 2

1989

Earlier work this paper cites.

Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)

Original

2013

Earlier work this paper cites.

Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: European conference on computer vision (2014)

Original

2014

Earlier work this paper cites.

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

2015

Earlier work this paper cites.

Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., Lipson, H.: Understanding neural networks through deep visualization. arXiv preprint arXiv:1506.06579 (2015)

Original

2015

Earlier work this paper cites.

Nguyen, A., Dosovitskiy, A., Yosinski, J., Brox, T., Clune, J.: Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In: Advances in neural information processing systems. pp. 3387–3395 (2016)

2016

Earlier work this paper cites.

Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you?: Explaining the predictions of any classifier. In: SIGKDD (2016)

2016

Earlier work this paper cites.

Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., Sayres, R.: Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav). arXiv preprint arXiv:1711.11279 (2017)

Original

2017

Cited alongside, same era.

Koh, P.W., Liang, P.: Understanding black-box predictions via influence functions. In: Proc. of Int. Conference on Machine Learning (2017)

2017

Cited alongside, same era.

Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: IEEE International Conference on Computer Vision (ICCV). pp. 618–626 (2017)

2017

Cited alongside, same era.

Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: Int. Conf. on Machine Learning (2017)

2017

Cited alongside, same era.

Kindermans, P.J., Hooker, S., Adebayo, J., Alber, M., Schütt, K.T., Dähne, S., Erhan, D., Kim, B.: The (un) reliability of saliency methods. In: Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (2019)

2019

Later among the works it cites.

Schneider, J., Handali, J.P.: Personalized explanation for machine learning: a conceptualization. In: European Conference on Information Systems (ECIS) (2019)

2019

Later among the works it cites.

Yang, F., Du, M., Hu, X.: Evaluating explanation without ground truth in interpretable machine learning. arXiv preprint arXiv:1907.06831 (2019)

Original

2019

Later among the works it cites.

van Doorenmalen, J., Menkovski, V.: Evaluation of cnn performance in semantically relevant latent spaces. In: Int. Symposium on Intelligent Data Analysis (2020)

2020

Closest in time.