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