I've just been reading

Zeiler, M.D. and Fergus, R., 2014, September. Visualizing and understanding convolutional networks. In European Conference on Computer Vision (pp. 818-833). Springer International Publishing. (Link, my summary)

and (partially)

Mahendran, A. and Vedaldi, A., 2016. Visualizing deep convolutional neural networks using natural pre-images. International Journal of Computer Vision, pp.1-23. (link, my summary)

They are both about visualizing features being learned in CNNs.

Although both papers have an introduction where you can read things like:

  • our understanding of [CNN features] remains limited
  • While the performance of representations has been improving significantly in the past few years, their design remains eminently empirical
  • In this paper, with the aim of obtaining a better understanding of representations, we develop a family of methods to investigate CNNs and other image features by means of visualizations
  • there is no clear understanding of why they perform so well, or how they might be improved
  • there is still little insight into the internal operation and behavior of these complex models, or how they achieve such good performance

Although I like the images, I don't see how these methods are better than simply pushing all images through the network and showing the top-$n$ images which activate the neuron of interest most. Was this evaluated? Did the authors have any insights into the features which other authors didn't have before / without those techniques?

(The Zeiler&Fergus paper at least added the occlusion sensitivity analysis which does help. However, a big part of the paper is this filter visualization by deconv-nets. And I don't see how this helps to address any of the points mentioned above)


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