Graph Convolutional Image Rotation Representation Without Augmentation
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Solution Overview
Problem
Existing convolutional neural networks (CNNs) struggle to achieve rotational invariance and equivariance, leading to inefficiencies in training and increased data requirements for augmentation, which limits their performance in applications requiring inference on arbitrarily rotated images.
Innovation Solution
The method employs a self-weighted nearest neighbors graph convolutional network (SWN-GCN) combined with global average pooling (GAP) to learn equivariant and invariant representations for image rotations, reducing reliance on data augmentation by leveraging structural properties of graph convolutional networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data augmentation is used to train neural networks for rotational invariance, then the network can learn rotation-invariant representations, but the dataset size increases exponentially and training complexity increases
Solution Approach 1:
The patent transforms the image data from spatial domain to frequency domain using Fourier transform, changing the parameter representation from spatial coordinates to frequency components. This transformation enables the network to inherently capture rotational equivariance through the spectral representation, avoiding the need for extensive data augmentation while maintaining rotational invariance performance
Solution Approach 2:
The patent replaces the mechanical data augmentation approach (physically rotating images to create training samples) with a mathematical transformation approach (Fourier transform to spectral domain). This substitution eliminates the need to generate multiple rotated versions of each image, significantly reducing dataset size while preserving rotational invariance through the spectral representation's inherent properties
2Reliability
If data augmentation is used to achieve rotational invariance, then the network can handle rotated images, but the training time and computational resources increase significantly
Solution Approach 1:
By changing the representation parameter from spatial pixels to frequency components, the patent enables the network to learn rotational invariance directly from the spectral representation without requiring multiple augmented training samples. This parameter transformation reduces training time by eliminating the need to process exponentially growing augmented datasets while maintaining the ability to handle rotated images
3Reliability
If deeper CNN layers are used to learn translation invariant features, then performance improves, but the network becomes more complex and harder to train
Solution Approach 1:
The patent substitutes the deep spatial convolutional architecture with a spectral graph convolutional network. By transforming images to the frequency domain and applying graph convolution operations on the spectral representation, the network achieves translation invariance and rotational equivariance with a shallower architecture, reducing training difficulty while maintaining performance
Data Source
AI summary
Disclosed are a method and apparatus for learning equivariant and invariant representations for rotation of an image based on a graph convolutional network. The method of learning an equivariant and invariant representation for rotation of an image based on a graph convolutional network performed by a computer device includes learning an equivariant representation for rotation of an image by using a self-weighted nearest neighbors graph convolutional network (SWN-GCN); and finally obtaining the equivariant representation for the rotation of the image obtained from the self-weighted nearest neighbors graph convolutional network as an invariant representation of the rotation of the image by using permutation invariance of global average pooling (GAP).