135 citations · 193 across the 12 of their papers we have counts for
8 papers · 1 filter
Generative Minimization Networks: Training GANs Without Competition
Paulina Grnarova, Yannic Kilcher, Kfir Y. Levy +2
Many applications in machine learning can be framed as minimization problems and solved efficiently using gradient-based techniques. However, recent applications of generative mode…
Rethinking Neural Networks With Benford's Law
Surya Kant Sahu, Abhinav Java, Arshad Shaikh +1
Benford's Law (BL) or the Significant Digit Law defines the probability distribution of the first digit of numerical values in a data sample. This Law is observed in many naturally…
Adversarial Training is a Form of Data-dependent Operator Norm Regularization
Kevin Roth, Yannic Kilcher, Thomas Hofmann
We establish a theoretical link between adversarial training and operator norm regularization for deep neural networks. Specifically, we prove that -norm constrained projec…
The Odds are Odd: A Statistical Test for Detecting Adversarial Examples
Kevin Roth, Yannic Kilcher, Thomas Hofmann
We investigate conditions under which test statistics exist that can reliably detect examples, which have been adversarially manipulated in a white-box attack. These statistics can…
The best defense is a good offense: Countering black box attacks by predicting slightly wrong labels
Yannic Kilcher, Thomas Hofmann
Black-Box attacks on machine learning models occur when an attacker, despite having no access to the inner workings of a model, can successfully craft an attack by means of model t…
Parametrizing filters of a CNN with a GAN
Yannic Kilcher, Gary Becigneul, Thomas Hofmann
It is commonly agreed that the use of relevant invariances as a good statistical bias is important in machine-learning. However, most approaches that explicitly incorporate invaria…
Flexible Prior Distributions for Deep Generative Models
Yannic Kilcher, Aurelien Lucchi, Thomas Hofmann
We consider the problem of training generative models with deep neural networks as generators, i.e. to map latent codes to data points. Whereas the dominant paradigm combines simpl…
Semantic Interpolation in Implicit Models
Yannic Kilcher, Aurelien Lucchi, Thomas Hofmann
In implicit models, one often interpolates between sampled points in latent space. As we show in this paper, care needs to be taken to match-up the distributional assumptions on co…