Training-time batch normalization increases expected local affine-region density in ReLU and piecewise-affine networks by acting as a batch-conditional recentering mechanism on switching hyperplanes.
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AffineLens enumerates the maximal continuous piecewise-affine regions induced by neural networks with batch-norm, pooling, residuals and convolutions inside a bounded input polytope and supplies visualizations and region-count metrics.
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Training-Time Batch Normalization Reshapes Local Partition Geometry in Piecewise-Affine Networks
Training-time batch normalization increases expected local affine-region density in ReLU and piecewise-affine networks by acting as a batch-conditional recentering mechanism on switching hyperplanes.
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AffineLens: Capturing the Continuous Piecewise Affine Functions of Neural Networks
AffineLens enumerates the maximal continuous piecewise-affine regions induced by neural networks with batch-norm, pooling, residuals and convolutions inside a bounded input polytope and supplies visualizations and region-count metrics.