Assessing Dataset Bias in Computer Vision. (arXiv:2205.01811v1 [cs.CV])

A biased dataset is a dataset that generally has attributes with an uneven
class distribution. These biases have the tendency to propagate to the models
that train on them, often leading to a poor performance in the minority class.
In this project, we will explore the extent to which various data augmentation
methods alleviate intrinsic biases within the dataset. We will apply several
augmentation techniques on a sample of the UTKFace dataset, such as
undersampling, geometric transformations, variational autoencoders (VAEs), …

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