149 citations · 338 across the 11 of their papers we have counts for
11 papers · 1 filter
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…
Excessive Invariance Causes Adversarial Vulnerability
Jörn-Henrik Jacobsen, Jens Behrmann, Richard Zemel +1
Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs. One core idea of adversarial example research is to reveal neura…
A rotation-equivariant convolutional neural network model of primary visual cortex
Alexander S. Ecker, Fabian H. Sinz, Emmanouil Froudarakis +7
Classical models describe primary visual cortex (V1) as a filter bank of orientation-selective linear-nonlinear (LN) or energy models, but these models fail to predict neural respo…
Generalisation in humans and deep neural networks
Robert Geirhos, Carlos R. Medina Temme, Jonas Rauber +3
We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations. First, using thr…
Adversarial Vision Challenge
Wieland Brendel, Jonas Rauber, Alexey Kurakin +5
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks…
Diverse feature visualizations reveal invariances in early layers of deep neural networks
Santiago A. Cadena, Marissa A. Weis, Leon A. Gatys +2
Visualizing features in deep neural networks (DNNs) can help understanding their computations. Many previous studies aimed to visualize the selectivity of individual units by findi…