Geared Rotationally Identical and Invariant Convolutional Neural Network Systems
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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. In this study, we further propose to compose a geared rotationally identical CNN system (GRI-CNN) with a small angle increment by connecting networks of participated processes at the first flatten layer. Using an ordinary CNN structure as a base, requirements for constructing a GRI-CNN include the use of either symmetric input vector or kernels with an angle increment that can form a complete cycle as a "gearwheel". Four basic GRI-CNN structures were studied. Each of them can produce quantitatively identical output results when a rotation angle of the input vector is evenly divisible by the increment angle of the gear. Our study showed when a rotated input vector does not match to a gear angle, the GRI-CNN can also produce a highly consistent result. With an ultra-fine increment angle (e.g., 1 degree or 0.1 degree), a virtually isotropic CNN system can be constructed.
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