  Convolutional Neural Networks (CNNs) are supposed to be fed with only
high-quality annotated datasets. Nonetheless, in many real-world scenarios,
such high quality is very hard to obtain, and datasets may be affected by any
sort of image degradation and mislabelling issues. This negatively impacts the
performance of standard CNNs, both during the training and the inference phase.
To address this issue we propose Wise2WipedNet (W2WNet), a new two-module
Convolutional Neural Network, where a Wise module exploits Bayesian inference
to identify and discard spurious images during the training, and a Wiped module
takes care of the final classification while broadcasting information on the
prediction confidence at inference time. The goodness of our solution is
demonstrated on a number of public benchmarks addressing different image
classification tasks, as well as on a real-world case study on histological
image analysis. Overall, our experiments demonstrate that W2WNet is able to
identify image degradation and mislabelling issues both at training and at
inference time, with a positive impact on the final classification accuracy.
