Trusting SVM for Piecewise Linear CNNs

@article{Berrada2016TrustingSF,
  title={Trusting SVM for Piecewise Linear CNNs},
  author={Leonard Berrada and Andrew Zisserman and M. Pawan Kumar},
  journal={ArXiv},
  year={2016},
  volume={abs/1611.02185}
}
We present a novel layerwise optimization algorithm for the learning objective of Piecewise-Linear Convolutional Neural Networks (PL-CNNs), a large class of convolutional neural networks. Specifically, PL-CNNs employ piecewise linear non-linearities such as the commonly used ReLU and max-pool, and an SVM classifier as the final layer. The key observation of our approach is that the prob- lem corresponding to the parameter estimation of a layer can be formulated as a difference-of-convex (DC… CONTINUE READING

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