Using and Abusing Equivariance: Investigating Differences between Exact and Approximate Equivariance in Computer Vision

In this work we show how Group Equivariant Convolutional Neural Networks use subsampling to learn to break equivariance to their symmetries. We focus on the 2D roto-translation group and investigate the impact of broken equivariance on network performance. We show that changing the input dimension of a network by as little as a single pixel can...

Using object-specific frequency information from labeled data to improve a CNN’s robustness to adversarial attacks

Convolutional Neural Networks are particularly vulnerable to attacks that manipulate their data, which are usually called adversarial attacks. In this paper, a method of filtering images using the Fast Fourier Transform is explored, along with its potential to be used as a defense mechanism to such attacks. The main contribution that differs...

Evaluating Methods for Improving Crowdsourced Annotations of Images Containing Lego Bricks

Data collection by means of crowdsourcing can be costly or produce inaccurate results. Methods have been proposed for solving these problems. However, it remains unclear what methods work best in scenarios with multiple similar objects of interest present in the same image, which is important for training computer vision with applications such...

The Current State of the Art in Multi-Label Image Classification Applied on LEGO Bricks

This paper shows how the current state of the art in image classification performs on LEGO bricks. Currently the standard image classification models with deep learning are single label image classifiers. In this paper we will convert them to work on multi-label images and subsequently evaluate how well they perform. We show how well the...