Rotational Equivariance for Object Classification Using xView


IEEE IGARSS’20 | Testing rotationally equivariant convolutional neural networks on overhead imagery data.

Lucius Bynum true


This work came out of a collaboration during my time as a Research Associate at Pacific Northwest National Laboratory. In the paper, we test a rotationally equivariant steerable filter convolutional neural network (SFCNN) on overhead imagery data. You can checkout the paper here.

Note: authors are sorted alphabetically.


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