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Current Deep Network (DN) visualization and interpretability methods relyheavily on data space visualizations such as scoring which dimensions of thedata are responsible for their associated prediction or generating new datafeatures or samples that best match a given DN unit or representation. In thispaper, we go one step further by developing the first provably exact method forcomputing the geometry of a DN's mapping - including its decision boundary -over a specified region of the data space. By leveraging the theory ofContinuous Piece-Wise Linear (CPWL) spline DNs, SplineCam exactly computes aDNs geometry without resorting to approximations such as sampling orarchitecture simplification. SplineCam applies to any DN architecture based onCPWL nonlinearities, including (leaky-)ReLU, absolute value, maxout, andmax-pooling and can also be applied to regression DNs such as implicit neuralrepresentations. Beyond decision boundary visualization and characterization,SplineCam enables one to compare architectures, measure generalizability andsample from the decision boundary on or off the manifold. Project Website:bit.ly/splinecam.

SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries