Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Deep neural networks have had an enormous impact on image analysis. State-of-the-art training methods, based on ight decay and DropOut, result in impressive performance when a very large training set is available. Hover, they tend to have large problems overfitting to small data sets. Indeed, the available regularization methods deal with the complexity of the network function only indirectly. ,  study the feasibility of directly using the $L_2$ function norm for regularization. Two methods to integrate this new regularization in the stochastic backpropagation are proposed. Moreover, the convergence of these new algorithms is studied.  finally show that they outperform the state-of-the-art methods in the low sample regime on benchmark datasets (MNIST and CIFAR10). The obtained results demonstrate very clear improvement, especially in the context of small sample regimes with data laying in a low dimensional manifold. Source code of the method can be found at \url{this https URL}.
['Amal Rannen Triki', 'Matthew B. Blaschko']
I am writing a research paper on a idea Stochastic Function Norm Regularization of Deep Networks
[{"from": "human", "value": "I am writing a research paper on a idea Stochastic Function Norm Regularization of Deep Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Deep neural networks have had an enormous impact on image analysis. State-of-the-art training methods, based on ight decay and DropOut, result in impressive performance when a very large training set is available. Hover, they tend to have large problems overfitting to small data sets. Indeed, the available regularization methods deal with the complexity of the network function only indirectly. ,  study the feasibility of directly using the $L_2$ function norm for regularization. Two methods to integrate this new regularization in the stochastic backpropagation are proposed. Moreover, the convergence of these new algorithms is studied.  finally show that they outperform the state-of-the-art methods in the low sample regime on benchmark datasets (MNIST and CIFAR10). The obtained results demonstrate very clear improvement, especially in the context of small sample regimes with data laying in a low dimensional manifold. Source code of the method can be found at \\url{this https URL}."}]