Regularization in deep neural networks

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Recent years have witnessed the great success of deep learning. As the deep architecture becomes larger and deeper, it is easy to overfit to relatively small amount of data. Regularization has proved to be an effective way to reduce overfitting in traditional statistical learning area. In the context of deep learning, some special design is required to regularize their training process. Generally, we firstly proposed a new regularization technique named “Shakeout” to improve the generalization ability of deep neural networks beyond Dropout, via introducing a combination of L₀, L₁, and L₂ regularization effect into the network training. Then we considered the unsupervised domain adaptation setting where the source domain data is labelled and the target domain data is unlabeled. We proposed “deep adversarial attention alignment” to regularize the behavior of the convolutional layers. Such regularization reduces the domain shift existing at the start in the convolutional layers which has been ignored by previous works and leads to superior adaptation results.
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