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cs.CV2022
Learning Continuous Rotation Canonicalization with Radial Beam Sampling
Johann Schmidt, Sebastian Stober
Nearly all state of the art vision models are sensitive to image rotations. Existing methods often compensate for missing inductive biases by using augmented training data to learn…
cs.LG2022
Visualizing Deep Neural Networks with Topographic Activation Maps
Valerie Krug, Raihan Kabir Ratul, Christopher Olson +1
Machine Learning with Deep Neural Networks (DNNs) has become a successful tool in solving tasks across various fields of application. However, the complexity of DNNs makes it diffi…