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Title: Jumpout: Improved Dropout for Deep Neural Networks with Rectified Linear Units

Paper content:
Dropout is a simple yet effective technique to improve generalization performance and prevent overfitting in deep neural networks (DNNs).
In this paper, we discuss three novel observations about dropout to better understand the generalization of DNNs with rectified linear unit (ReLU) activations: 1) dropout is a smoothing technique that encourages each local linear model of a DNN to be trained on data points from nearby regions; 2) a constant dropout rate can result in effective neural-deactivation rates that are significantly different for layers with different fractions of activated neurons; and 3) the rescaling factor of dropout causes an inconsistency to occur between the normalization during training and testing conditions when batch normalization is also used.
The above leads to three simple but nontrivial improvements to dropout resulting in our proposed method "Jumpout.
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Jumpout samples the dropout rate using a monotone decreasing distribution (such as the right part of a truncated Gaussian), so the local linear model at each data point is trained, with high probability, to work better for data points from nearby than from more distant regions.
Instead of tuning a dropout rate for each layer and applying it to all samples, jumpout moreover adaptively normalizes the dropout rate at each layer and every training sample/batch, so the effective dropout rate applied to the activated neurons are kept the same.
Moreover, we rescale the outputs of jumpout for a better trade-off that keeps both the variance and mean of neurons more consistent between training and test phases, which mitigates the incompatibility between dropout and batch normalization.
Compared to the original dropout, jumpout shows significantly improved performance on CIFAR10, CIFAR100, Fashion- MNIST, STL10, SVHN, ImageNet-1k, etc., while introducing negligible additional memory and computation costs.
Deep learning has achieved remarkable success on a variety of machine learning tasks BID15 BID14 .
Deep neural networks (DNN), however, are often able to fit the training data perfectly -this can result in the overfitting problem, thereby weakening the generalization performance on unseen data.
Dropout BID17 BID7 is a simple yet effective technique to mitigate such problems by randomly setting the activations of hidden neurons to 0, a strategy that reduces co-adaptation amongst neurons.
Dropout applies to any layer in a DNN without causing significant additional computational overhead.Dropout, however, has several drawbacks.
Firstly, dropout rates, constituting extra hyper-parameters at each layer, need to be tuned to get optimal performance.
Too high a dropout rate can slow the convergence rate of the model, and often hurt final performance.
Too low a rate yields few or no improvements on generalization performance.
Ideally, dropout rates should be tuned separately for each layer and also during various training stages.
In practice, to reduce computation, we often tune a single dropout rate and keep it constant for all dropout layers and throughout the training process.If we treat dropout as a type of perturbation on each training sample, it acts to generalize the DNN to noisy samples having that specific expected amount of perturbation (due to the fixed dropout rate) with high probability.
The fixed rate rules out samples typical having less perturbation, i.e., those potentially more likely to be closer to the original samples and thus that are potentially more helpful to improve generalization.
Also, when a constant dropout rate is applied to layers and samples having different fractions of activated neurons, the effective dropout rate (i.e., the proportion of the activated neurons that are deactivated by dropout) varies, which might result in too much perturbation for some layers and samples and too little perturbation for others.Another deficiency of dropout lies in its incompatibility with batch normalization (BN) BID8 (more empirical evidence of this is shown in Section 3.3).
As dropout randomly shuts down activated neurons, it needs to rescale the undropped neurons to match the original overall activation gain of the layer.
Unfortunately, such rescaling breaks the consistency of the normalization parameters required between training and test phases 1 and may cause poor behavior when used with BN.
Since BN, and its variants BID0 BID18 BID20 , has become an almost indispensable component of modern DNN architectures to keep the training stable and to accelerate convergence, dropout itself often gets dropped out in the choice between these two non-complementary options and has recently become less popular.

assistant: text: Jumpout applies three simple yet effective modifications to dropout, based on novel understandings about the generalization performance of DNN with ReLU in local regions.