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Smaller Models, Better Generalization
Reducing network complexity has been a major research focus in recent years with the advent of mobile technology.
Learning Neural Network Classifiers with Low Model Complexity
Our proposed approach yields benefits across a wide range of architectures, in comparison to and in conjunction with methods such as Dropout and Batch Normalization, and our results strongly suggest that deep learning techniques can benefit from model complexity control methods such as the LCNN learning rule.
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