Data augmentation produces well-behaved trajectories in shape-invariant representation space, with augmentation type steering distinct directions and geometry predicting ensembling gains.
A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information
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How Data Augmentation Shapes Neural Representations
Data augmentation produces well-behaved trajectories in shape-invariant representation space, with augmentation type steering distinct directions and geometry predicting ensembling gains.