Search Results for author: Ivan Sosnovik

Found 9 papers, 4 papers with code

Wiggling Weights to Improve the Robustness of Classifiers

no code implementations18 Nov 2021 Sadaf Gulshad, Ivan Sosnovik, Arnold Smeulders

To demonstrate that wiggling the weights consistently improves classification, we choose a standard network and modify it to a transform-augmented network.

PIE: Pseudo-Invertible Encoder

no code implementations ICLR 2019 Jan Jetze Beitler, Ivan Sosnovik, Arnold Smeulders

We consider the problem of information compression from high dimensional data.

Two is a crowd: tracking relations in videos

no code implementations11 Aug 2021 Artem Moskalev, Ivan Sosnovik, Arnold Smeulders

Tracking multiple objects individually differs from tracking groups of related objects.

Built-in Elastic Transformations for Improved Robustness

no code implementations20 Jul 2021 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.

Data Augmentation

DISCO: accurate Discrete Scale Convolutions

1 code implementation4 Jun 2021 Ivan Sosnovik, Artem Moskalev, Arnold Smeulders

In recent work scale equivariance was added to convolutional neural networks.

Scale Equivariance Improves Siamese Tracking

1 code implementation17 Jul 2020 Ivan Sosnovik, Artem Moskalev, Arnold Smeulders

We develop the theory for scale-equivariant Siamese trackers, and provide a simple recipe for how to make a wide range of existing trackers scale-equivariant.

Translation Visual Object Tracking +1

Scale-Equivariant Steerable Networks

no code implementations ICLR 2020 Ivan Sosnovik, Michał Szmaja, Arnold Smeulders

The effectiveness of Convolutional Neural Networks (CNNs) has been substantially attributed to their built-in property of translation equivariance.

Image Classification Translation

Neural networks for topology optimization

2 code implementations27 Sep 2017 Ivan Sosnovik, Ivan Oseledets

The main novelty of this work is to state the problem as an image segmentation task.

Semantic Segmentation

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