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

arXiv 2110.04070 ↗First posted 5 Oct 2021cs.CV cs.LGcited by 0

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Abstract (arXiv)

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.

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References · 11

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  7. 2020Image Quality Assessment: Unifying Structure and Texture Similaritycited by 792
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  9. 2016Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learningcited by 209
  10. 2020Comparing Different Deep Learning Architectures for Classification of Chest Radiographscited by 24
  11. 2019Why do These Match? Explaining the Behavior of Image Similarity Modelscited by 10

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