LDMNet: Low Dimensional Manifold Regularized Neural Networks

CVPR 2018 Wei ZhuQiang QiuJiaji HuangRobert CalderbankGuillermo SapiroIngrid Daubechies

Deep neural networks have proved very successful on archetypal tasks for which large training sets are available, but when the training data are scarce, their performance suffers from overfitting. Many existing methods of reducing overfitting are data-independent, and their efficacy is often limited when the training set is very small... (read more)

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