  Image classification has become a ubiquitous task. Models trained on good
quality data achieve accuracy which in some application domains is already
above human-level performance. Unfortunately, real-world data are quite often
degenerated by the noise existing in features and/or labels. There are quite
many papers that handle the problem of either feature or label noise,
separately. However, to the best of our knowledge, this piece of research is
the first attempt to address the problem of concurrent occurrence of both types
of noise. Basing on the MNIST, CIFAR-10 and CIFAR-100 datasets, we
experimentally proved that the difference by which committees beat single
models increases along with noise level, no matter it is an attribute or label
disruption. Thus, it makes ensembles legitimate to be applied to noisy images
with noisy labels. The aforementioned committees' advantage over single models
is positively correlated with dataset difficulty level as well. We propose
three committee selection algorithms that outperform a strong baseline
algorithm which relies on an ensemble of individual (nonassociated) best
models.
