# On the Importance of Capturing a Sufficient Diversity of Perspective for the Classification of micro-PCBs

arXiv: https://arxiv.org/abs/2101.11164 · HTML: https://mlsanity.com/paper/2101.11164/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [Group Equivariant Convolutional Networks](https://mlsanity.com/paper/1602.07576/) (2016)
- [An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution](https://mlsanity.com/paper/1807.03247/) (2018)
- [Understanding image representations by measuring their equivariance and equivalence](https://mlsanity.com/paper/1411.5908/) (2014)
- [Non-convex Optimization for Machine Learning](https://mlsanity.com/paper/1712.07897/) (2017)
- [On Graduated Optimization for Stochastic Non-Convex Problems](https://mlsanity.com/paper/1503.03712/) (2015)
- [A PCB Dataset for Defects Detection and Classification](https://mlsanity.com/paper/1901.08204/) (2019)
- [Homogeneous Vector Capsules Enable Adaptive Gradient Descent in Convolutional Neural Networks](https://mlsanity.com/paper/1906.08676/) (2019)

## Built on by (most cited first, 2 of 2)

- [Towards an Analytical Definition of Sufficient Data](https://mlsanity.com/paper/2202.03238/) (2022)
- [Class Density and Dataset Quality in High-Dimensional, Unstructured Data](https://mlsanity.com/paper/2202.03856/) (2022)

## Abstract (arXiv)

We present a dataset consisting of high-resolution images of 13 micro-PCBs captured in various rotations and perspectives relative to the camera, with each sample labeled for PCB type, rotation category, and perspective categories. We then present the design and results of experimentation on combinations of rotations and perspectives used during training and the resulting impact on test accuracy. We then show when and how well data augmentation techniques are capable of simulating rotations vs. perspectives not present in the training data. We perform all experiments using CNNs with and without homogeneous vector capsules (HVCs) and investigate and show the capsules' ability to better encode the equivariance of the sub-components of the micro-PCBs. The results of our experiments lead us to conclude that training a neural network equipped with HVCs, capable of modeling equivariance among sub-components, coupled with training on a diversity of perspectives, achieves the greatest classification accuracy on micro-PCB data.