Check out our latest research on weakly-supervised 3D shape completion.


Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, Rob Fergus. Intriguing properties of neural networks. CoRR abs/1312.6199, 2013.

Szegedy et al. were (to the best of my knowledge) the first to describe the phenomen of adversarial examples as researched today. Specifically, they described the main objective in order to obtain adversarial examples as

$\arg\min_r \|r\|_2$ s.t. $f(x+r)=l$ and $x+r$ being a valid image

where $f$ is the neural network and $l$ the target class (i.e. targeted adversarial example). In the paper, they originally headlined the section by “blind spots in neural networks”. While they give some explanation and provide experiments, also introducing the notion of transferability of adversarial examples and an idea of adversarial examples used as regularization during training, many questions are left open. The given conclusion, that these adversarial examples are highly unlikely and that these examples lie dense within regular training examples are controversial in the literature.

Also find this summary on ShortScience.org.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below or get in touch with me: