  Large scale image classification datasets often contain noisy labels. We take
a principled probabilistic approach to modelling input-dependent, also known as
heteroscedastic, label noise in these datasets. We place a multivariate Normal
distributed latent variable on the final hidden layer of a neural network
classifier. The covariance matrix of this latent variable, models the aleatoric
uncertainty due to label noise. We demonstrate that the learned covariance
structure captures known sources of label noise between semantically similar
and co-occurring classes. Compared to standard neural network training and
other baselines, we show significantly improved accuracy on Imagenet ILSVRC
2012 79.3% (+2.6%), Imagenet-21k 47.0% (+1.1%) and JFT 64.7% (+1.6%). We set a
new state-of-the-art result on WebVision 1.0 with 76.6% top-1 accuracy. These
datasets range from over 1M to over 300M training examples and from 1k classes
to more than 21k classes. Our method is simple to use, and we provide an
implementation that is a drop-in replacement for the final fully-connected
layer in a deep classifier.
