75 citations · 231 across the 9 of their papers we have counts for
9 papers
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
David Klindt, Lukas Schott, Yash Sharma +4
We construct an unsupervised learning model that achieves nonlinear disentanglement of underlying factors of variation in naturalistic videos. Previous work suggests that represent…
Towards causal generative scene models via competition of experts
Julius von Kügelgen, Ivan Ustyuzhaninov, Peter Gehler +2
Learning how to model complex scenes in a modular way with recombinable components is a pre-requisite for higher-order reasoning and acting in the physical world. However, current…
Compositional uncertainty in deep Gaussian processes
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser +3
Gaussian processes (GPs) are nonparametric priors over functions. Fitting a GP implies computing a posterior distribution of functions consistent with the observed data. Similarly,…
Accurate, reliable and fast robustness evaluation
Wieland Brendel, Jonas Rauber, Matthias Kümmerer +2
Throughout the past five years, the susceptibility of neural networks to minimal adversarial perturbations has moved from a peculiar phenomenon to a core issue in Deep Learning. De…
Monotonic Gaussian Process Flow
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek +1
We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations. We derive a…
Sequence Alignment with Dirichlet Process Mixtures
Ieva Kazlauskaite, Ivan Ustyuzhaninov, Carl Henrik Ek +1
We present a probabilistic model for unsupervised alignment of high-dimensional time-warped sequences based on the Dirichlet Process Mixture Model (DPMM). We follow the approach in…
One-Shot Instance Segmentation
Claudio Michaelis, Ivan Ustyuzhaninov, Matthias Bethge +1
We tackle the problem of one-shot instance segmentation: Given an example image of a novel, previously unknown object category, find and segment all objects of this category within…
One-shot Texture Segmentation
Ivan Ustyuzhaninov, Claudio Michaelis, Wieland Brendel +1
We introduce one-shot texture segmentation: the task of segmenting an input image containing multiple textures given a patch of a reference texture. This task is designed to turn t…
Texture Synthesis Using Shallow Convolutional Networks with Random Filters
Ivan Ustyuzhaninov, Wieland Brendel, Leon A. Gatys +1
Here we demonstrate that the feature space of random shallow convolutional neural networks (CNNs) can serve as a surprisingly good model of natural textures. Patches from the same…