Summary of Variational Partial Group Convolutions For Input-aware Partial Equivariance Of Rotations and Color-shifts, by Hyunsu Kim et al.
Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts
by Hyunsu Kim, Yegon Kim, Hongseok Yang, Juho Lee
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to group equivariant convolutional neural networks (G-CNNs) that captures varying levels of partial equivariance specific to each data instance. The current G-CNNs are fixed to the symmetry of the whole group, limiting their adaptability to real-world datasets with diverse partial symmetries. To address this limitation, the authors introduce Variational Partial G-CNN (VP G-CNN), which redesigns the distribution of output group elements to be conditioned on input data using variational inference to avoid overfitting. This enables the model to adjust its equivariance levels according to individual data points’ needs. The paper also addresses training instability in discrete group equivariance models by redesigning the reparametrizable distribution. The authors demonstrate VP G-CNN’s effectiveness on toy and real-world datasets, including MNIST67-180, CIFAR10, ColorMNIST, and Flowers102, showcasing robust performance even in uncertainty metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make group equivariant CNNs better at handling different kinds of symmetry in data. Right now, these networks are stuck with just one kind of symmetry, which limits how well they can work on real-world datasets. The authors introduce an approach called Variational Partial G-CNN that adjusts its symmetry level based on each individual piece of data. This helps the network avoid overfitting and learn more effectively from uncertain or noisy data. The paper shows that this new approach works well on several different kinds of data, including images of handwritten digits and flowers. |
Keywords
» Artificial intelligence » Cnn » Inference » Overfitting
