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Equivariant networks are specifically designed to ensure consistent behavior with respect to a set of input transformations, leading to higher sample efficiency and more accurate and robust predictions.
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Group equivariant convolutional networks
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Mask r-cnn
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Frame averaging for invariant and equivariant network design
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Rotationally equivariant 3d object detection
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Equivariance with learned canonicalization functions
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The lie derivative for measuring learned equivariance
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A program to build e(n)-equivariant steerable CNNs
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Equi-tuning: Group equivariant fine-tuning of pretrained models
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