  Due to the over-emphasize of the quantity of data, the data quality has often
been overlooked. However, not all training data points contribute equally to
learning. In particular, if mislabeled, it might actively damage the
performance of the model and the ability to generalize out of distribution, as
the model might end up learning spurious artifacts present in the dataset. This
problem gets compounded by the prevalence of heavily parameterized and complex
deep neural networks, which can, with their high capacity, end up memorizing
the noise present in the dataset. This paper proposes a novel statistic --
noise score, as a measure for the quality of each data point to identify such
mislabeled samples based on the variations in the latent space representation.
In our work, we use the representations derived by the inference network of
data quality supervised variational autoencoder (AQUAVS). Our method leverages
the fact that samples belonging to the same class will have similar latent
representations. Therefore, by identifying the outliers in the latent space, we
can find the mislabeled samples. We validate our proposed statistic through
experimentation by corrupting MNIST, FashionMNIST, and CIFAR10/100 datasets in
different noise settings for the task of identifying mislabelled samples. We
further show significant improvements in accuracy for the classification task
for each dataset.
