TITLE: Measures of Complexity for Large Scale Image Datasets 

ABSTRACT: Large scale image datasets are a growing trend in the field of machine
learning. However, it is hard to quantitatively understand or specify how
various datasets compare to each other - i.e., if one dataset is more complex
or harder to ``learn'' with respect to a deep-learning based network. In this
work, we build a series of relatively computationally simple methods to measure
the complexity of a dataset. Furthermore, we present an approach to demonstrate
visualizations of high dimensional data, in order to assist with visual
comparison of datasets. We present our analysis using four datasets from the
autonomous driving research community - Cityscapes, IDD, BDD and Vistas. Using
entropy based metrics, we present a rank-order complexity of these datasets,
which we compare with an established rank-order with respect to deep learning.
Large scale image datasets are a growing trend in the field of machine
learning. However, it is hard to quantitatively understand or specify how
various datasets compare to each other - i.e., if one dataset is more complex
or harder to ``learn'' with respect to a deep-learning based network. In this
work, we build a series of relatively computationally simple methods to measure
the complexity of a dataset. Furthermore, we present an approach to demonstrate
visualizations of high dimensional data, in order to assist with visual
comparison of datasets. We present our analysis using four datasets from the
autonomous driving research community - Cityscapes, IDD, BDD and Vistas. Using
entropy based metrics, we present a rank-order complexity of these datasets,
which we compare with an established rank-order with respect to deep learning.
Measures of Complexity for Large Scale Image Datasets
http://arxiv.org/abs/2008.04431v1