What is the difference between DropOut and DropConnect methods of regularization?

What is the difference between DropOut and DropConnect methods of regularization?

When training with Dropout, a randomly selected subset of activations are set to zero within each layer. DropConnect instead sets a randomly selected subset of weights within the network to zero. Each unit thus receives input from a random subset of units in the previous layer.

Is DropConnect better than DropOut?

DropConnect is a generalization of DropOut because it produces even more possible models, since there are almost always more connections than units. However, you can get similar outcomes on an individual trial.

What happens if we apply DropOut to the individual weights of the weight matrix rather than the activations?

By appling dropout to input weights rather than the activations, DropConnect generalizes to the entire connectivity structure of a fully connected neural network layer. A neuron takes as an input a series of weights and applies a non-linear activation function to generate an output.

What is the reason for using dropout?

— Dropout: A Simple Way to Prevent Neural Networks from Overfitting, 2014. Because the outputs of a layer under dropout are randomly subsampled, it has the effect of reducing the capacity or thinning the network during training. As such, a wider network, e.g. more nodes, may be required when using dropout.

What is the difference between dropout and Drop Connect?

DropOut and DropConnect are both methods intended to prevent “co-adaptation” of units in a neural network. In other words, we want units to independently extract features from their inputs instead of relying on other neurons to do so. Suppose we have a multilayered feedforward network like this one (the topology doesn’t really matter).

Can I use’tf.nn.dropout’to implement DropConnect?

I (think) that I grasp the basics of DropOut and the use of the TensorFlow API in implementing it. But the normalization that’s linked to the dropout probability in tf.nn.dropout seems not to be a part of DropConnect. Is that correct? If so, does normalizing do any “harm” or can I simply apply tf.nn.dropout to my weights to implement DropConnect?

How does dropout affect the capacity of a network?

Because the outputs of a layer under dropout are randomly subsampled, it has the effect of reducing the capacity or thinning the network during training. As such, a wider network, e.g. more nodes, may be required when using dropout.

What is dropout and how is it used in neural networks?

— Dropout: A Simple Way to Prevent Neural Networks from Overfitting, 2014. A new hyperparameter is introduced that specifies the probability at which outputs of the layer are dropped out, or inversely, the probability at which outputs of the layer are retained.