Stochastic Depth aims to shrink the depth of a network during training, while keeping it unchanged during testing. This is achieved by randomly dropping entire ResBlocks during training and bypassing their transformations through skip connections.
Let $b_{l} \in$ {$0, 1$} denote a Bernoulli random variable, which indicates whether the $l$th ResBlock is active ($b_{l} = 1$) or inactive ($b_{l} = 0$). Further, let us denote the “survival” probability of ResBlock $l$ as $p_{l} = \text{Pr}\left(b_{l} = 1\right)$. With this definition we can bypass the $l$th ResBlock by multiplying its function $f_{l}$ with $b_{l}$ and we extend the update rule to:
$$ H_{l} = \text{ReLU}\left(b_{l}f_{l}\left(H_{l1}\right) + \text{id}\left(H_{l1}\right)\right) $$
If $b_{l} = 1$, this reduces to the original ResNet update and this ResBlock remains unchanged. If $b_{l} = 0$, the ResBlock reduces to the identity function, $H_{l} = \text{id}\left((H_{l}−1\right)$.
Source: Deep Networks with Stochastic DepthPaper  Code  Results  Date  Stars 

Task  Papers  Share 

Semantic Segmentation  70  11.99% 
Image Classification  52  8.90% 
Object Detection  43  7.36% 
Instance Segmentation  21  3.60% 
Image Segmentation  18  3.08% 
Medical Image Segmentation  17  2.91% 
SuperResolution  16  2.74% 
Classification  12  2.05% 
SelfSupervised Learning  11  1.88% 
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