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  85  9.96% 
Image Classification  58  6.80% 
Object Detection  50  5.86% 
Decoder  43  5.04% 
Image Segmentation  25  2.93% 
Instance Segmentation  25  2.93% 
Medical Image Segmentation  20  2.34% 
SuperResolution  20  2.34% 
Object  20  2.34% 
Component  Type 


🤖 No Components Found  You can add them if they exist; e.g. Mask RCNN uses RoIAlign 