  Deep neural networks (DNNs) have been used to create models for many complex
analysis problems like image recognition and medical diagnosis. DNNs are a
popular tool within machine learning due to their ability to model complex
patterns and distributions. However, the performance of these networks is
highly dependent on the quality of the data used to train the models. Two
characteristics of these sets, noisy labels and training set biases, are known
to frequently cause poor generalization performance as a result of overfitting
to the training set. This paper aims to solve this problem using the approach
proposed by Ren et al. (2018) using meta-training and online weight
approximation. We will first implement a toy-problem to crudely verify the
claims made by the authors of Ren et al. (2018) and then venture into using the
approach to solve a real world problem of Skin-cancer detection using an
imbalanced image dataset.
