**Fraternal Dropout** is a regularization method for recurrent neural networks that trains two identical copies of an RNN (that share parameters) with different [dropout](https://paperswithcode.com/method/dropout) masks while minimizing the difference between their (pre-[softmax](https://paperswithcode.com/method/softmax)) predictions. This encourages the representations of RNNs to be invariant to dropout mask, thus being robust.

**Darknet-19** is a convolutional neural network that is used as the backbone of [YOLOv2](https://paperswithcode.com/method/yolov2).  Similar to the [VGG](https://paperswithcode.com/method/vgg) models it mostly uses $3 \times 3$ filters and doubles the number of channels after every pooling step. Following the work on Network in Network (NIN) it uses [global average pooling](https://paperswithcode.com/method/global-average-pooling) to make predictions as well as $1 \times 1$ filters to compress the feature representation between $3 \times 3$ convolutions. [Batch Normalization](https://paperswithcode.com/method/batch-normalization) is used to stabilize training, speed up convergence, and regularize the model batch.

Darknet-19

YOLO9000: Better, Faster, Stronger

Fraternal Dropout

R-Mix (Random Mix-up) is a Mix-up family Data Augmentation method. It combines random Mix-up with Saliency-guided mix-up, producing a procedure that is fast and performant, while reserving good characteristics of Saliency-guided Mix-up such as low Expected Calibration Error and high Weakly-supervised Object Localization accuracy.

Source	Fraternal Dropout
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com