Towards Learning Affine-Invariant Representations via Data-Efficient CNNs

no code implementations • 31 Aug 2019 • Xenju Xu, Guanghui Wang, Alan Sullivan, Ziming Zhang

In this paper we propose integrating a priori knowledge into both design and training of convolutional neural networks (CNNs) to learn object representations that are invariant to affine transformations (i. e., translation, scale, rotation).

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