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Deep Neural Networks with Efficient Guaranteed Invariances
We then address the problem of incorporating multiple desired invariances into a single network.
Improving the Sample-Complexity of Deep Classification Networks with Invariant Integration
We demonstrate the improved sample complexity on the Rotated-MNIST, SVHN and CIFAR-10 datasets where rotation-invariant-integration-based Wide-ResNet architectures using monomials and weighted sums outperform the respective baselines in the limited sample regime.
Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey
One promising approach, inspired by the success of convolutional neural networks in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations of the problem to solve that affect the output in a predictable way.
Invariant Integration in Deep Convolutional Feature Space
In this contribution, we show how to incorporate prior knowledge to a deep neural network architecture in a principled manner.
