43 citations · 76 across the 10 of their papers we have counts for
5 papers · 1 filter
Wiggling Weights to Improve the Robustness of Classifiers
Sadaf Gulshad, Ivan Sosnovik, Arnold Smeulders
Robustness against unwanted perturbations is an important aspect of deploying neural network classifiers in the real world. Common natural perturbations include noise, saturation,…
PIE: Pseudo-Invertible Encoder
Jan Jetze Beitler, Ivan Sosnovik, Arnold Smeulders
We consider the problem of information compression from high dimensional data. Where many studies consider the problem of compression by non-invertible transformations, we emphasiz…
Two is a crowd: tracking relations in videos
Artem Moskalev, Ivan Sosnovik, Arnold Smeulders
Tracking multiple objects individually differs from tracking groups of related objects. When an object is a part of the group, its trajectory depends on the trajectories of the oth…
Built-in Elastic Transformations for Improved Robustness
Sadaf Gulshad, Ivan Sosnovik, Arnold Smeulders
We focus on building robustness in the convolutions of neural visual classifiers, especially against natural perturbations like elastic deformations, occlusions and Gaussian noise.…
DISCO: accurate Discrete Scale Convolutions
Ivan Sosnovik, Artem Moskalev, Arnold Smeulders
Scale is often seen as a given, disturbing factor in many vision tasks. When doing so it is one of the factors why we need more data during learning. In recent work scale equivaria…