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Geared Rotationally Identical and Invariant Convolutional Neural Network Systems
Theorems and techniques to form different types of transformationally invariant processing and to produce the same output quantitatively based on either transformationally invariant operators or symmetric operations have recently been introduced by the authors.
Transformationally Identical and Invariant Convolutional Neural Networks by Combining Symmetric Operations or Input Vectors
Other transformationally identical CNNs can be constructed by averaging transformed input vectors of the family at the input layer followed by an ordinary CNN process or by a set of symmetric operations.
Transformationally Identical and Invariant Convolutional Neural Networks through Symmetric Element Operators
We further found that any CNN possessing the same TI kernel property for all convolution layers followed by a flatten layer with weight sharing among their transformation corresponding elements would output the same result for all transformation versions of the original input vector.
