jm + image-recognition   2

Universal adversarial perturbations
in today’s paper Moosavi-Dezfooli et al., show us how to create a _single_ perturbation that causes the vast majority of input images to be misclassified.
adversarial-classification  spam  image-recognition  ml  machine-learning  dnns  neural-networks  images  classification  perturbation  papers 
5 weeks ago by jm
jwz on Inceptionism
"Shoggoth ovipositors":
So then they reach inside to one of the layers and spin the knob randomly to fuck it up. Lower layers are edges and curves. Higher layers are faces, eyes and shoggoth ovipositors. [....] But the best part is not when they just glitch an image -- which is a fun kind of embossing at one end, and the "extra eyes" filter at the other -- but is when they take a net trained on some particular set of objects and feed it static, then zoom in, and feed the output back in repeatedly. That's when you converge upon the platonic ideal of those objects, which -- it turns out -- tend to be Giger nightmare landscapes. Who knew. (I knew.)


This stuff is still boggling my mind. All those doggy faces! That is one dog-obsessed ANN.
neural-networks  ai  jwz  funny  shoggoths  image-recognition  hr-giger  art  inceptionism 
june 2015 by jm

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