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Do deep Nets really need weight decay and dropout?
This overparameterization is often said to be controlled with the help of different regularization techniques, mainly weight decay and dropout. However, since these techniques reduce the effective capacity of the model, typically even deeper and wider architectures are required to compensate for the reduced capacity.
What does dropout do in neural network?
— Dropout: A Simple Way to Prevent Neural Networks from Overfitting, 2014. Dropout simulates a sparse activation from a given layer, which interestingly, in turn, encourages the network to actually learn a sparse representation as a side-effect.
Why do dropouts avoid overfitting?
Dropout prevents overfitting due to a layer’s “over-reliance” on a few of its inputs. Because these inputs aren’t always present during training (i.e. they are dropped at random), the layer learns to use all of its inputs, improving generalization.
How are dropout and weight decay used in CNN?
Dropout, weight decay and data augmentation have all become part and parcel of every CNN developers standard toolkit. The assumption has been that each contribute in a hopefully synergistic manner towards producing an optimal CNN.
What do you need to know about weight decay?
Weight Decay 4.6. Dropout 4.7. Forward Propagation, Backward Propagation, and Computational Graphs 4.8. Numerical Stability and Initialization 4.9. Environment and Distribution Shift 4.10. Predicting House Prices on Kaggle 5.
Which is better dropout or filter norm constraints?
Dropout works well in practice, perhaps replacing the need for weight regularization (e.g. weight decay) and activity regularization (e.g. representation sparsity). … dropout is more effective than other standard computationally inexpensive regularizers, such as weight decay, filter norm constraints and sparse activity regularization.
Which is better data augmentation or weight decay?
Below is a chart showing the performance comparisons: Image from Garcia-Konig paper — purple is augmentation alone, red is augmentation + weight decay and Dropout. For the ‘all CNN’ category, augmentation alone consistently outperforms. Tested on ImageNet, CIFAR-10 and CIFAR-100.