# R-Drop: Regularized Dropout for Neural Networks

R-drop is a simple yet very effective regularization method built upon dropout, by minimizing the bidirectional KL-divergence of the output distributions of any pair of sub models sampled from dropout in model training.

## Usage:

R-Drop is an almost universal method for supervised tasks and even performs well for semi-supervised setting. For other settings and tasks that are not mentioned in our paper, feel free to try the following piece of code.

```
import torch.nn.functional as F
# define your task model, which outputs the classifier logits
model = TaskModel()
def compute_kl_loss(self, p, q pad_mask=None):
p_loss = F.kl_div(F.log_softmax(p, dim=-1), F.softmax(q, dim=-1), reduction='none')
q_loss = F.kl_div(F.log_softmax(q, dim=-1), F.softmax(p, dim=-1), reduction='none')
# pad_mask is for seq-level tasks
if pad_mask is not None:
p_loss.masked_fill_(pad_mask, 0.)
q_loss.masked_fill_(pad_mask, 0.)
# You can choose whether to use function "sum" and "mean" depending on your task
p_loss = p_loss.sum()
q_loss = q_loss.sum()
loss = (p_loss + q_loss) / 2
return loss
# keep dropout and forward twice
logits = model(x)
logits2 = model(x)
# cross entropy loss for classifier
ce_loss = 0.5 * (cross_entropy_loss(logits, label) + cross_entropy_loss(logits2, label))
kl_loss = compute_kl_loss(logits, logits2)
# carefully choose hyper-parameters
loss = ce_loss + α * kl_loss
```

## Quick Links:

R-Drop is capable to handle many tasks for both NLP and CV: