TITLE: Dataset Structural Index: Leveraging a machine's perspective towards visual data 

ABSTRACT: With advances in vision and perception architectures, we have realized that
working with data is equally crucial, if not more, than the algorithms. Till
today, we have trained machines based on our knowledge and perspective of the
world. The entire concept of Dataset Structural Index(DSI) revolves around
understanding a machine`s perspective of the dataset. With DSI, I show two meta
values with which we can get more information over a visual dataset and use it
to optimize data, create better architectures, and have an ability to guess
which model would work best. These two values are the Variety contribution
ratio and Similarity matrix. In the paper, I show many applications of DSI, one
of which is how the same level of accuracy can be achieved with the same model
architectures trained over less amount of data.
With advances in vision and perception architectures, we have realized that
working with data is equally crucial, if not more, than the algorithms. Till
today, we have trained machines based on our knowledge and perspective of the
world. The entire concept of Dataset Structural Index(DSI) revolves around
understanding a machine`s perspective of the dataset. With DSI, I show two meta
values with which we can get more information over a visual dataset and use it
to optimize data, create better architectures, and have an ability to guess
which model would work best. These two values are the Variety contribution
ratio and Similarity matrix. In the paper, I show many applications of DSI, one
of which is how the same level of accuracy can be achieved with the same model
architectures trained over less amount of data.
Dataset Structural Index: Leveraging a machine's perspective towards visual data
http://arxiv.org/abs/2110.04070v3