  Deep Learning systems have shown tremendous accuracy in image classification,
at the cost of big image datasets. Collecting such amounts of data can lead to
labelling errors in the training set. Indexing multimedia content for
retrieval, classification or recommendation can involve tagging or
classification based on multiple criteria. In our case, we train face
recognition systems for actors identification with a closed set of identities
while being exposed to a significant number of perturbators (actors unknown to
our database). Face classifiers are known to be sensitive to label noise. We
review recent works on how to manage noisy annotations when training deep
learning classifiers, independently from our interest in face recognition.
