DropPath
DropPath
Just as [dropout](https://paperswithcode.com/method/dropout) prevents co-adaptation of activations, **DropPath** prevents co-adaptation of parallel paths in networks such as [FractalNets](https://paperswithcode.com/method/fractalnet) by randomly dropping operands of the join layers. This
discourages the network from using one input path as an anchor and another as a corrective term (a
configuration that, if not prevented, is prone to overfitting). Two sampling strategies are:

- **Local**: a join drops each input with fixed probability, but we make sure at least one survives.
- **Global**: a single path is selected for the entire network. We restrict this path to be a single
column, thereby promoting individual columns as independently strong predictors.
FractalNet: Ultra-Deep Neural Networks without Residuals
https://paperswithcode.com/paper/fractalnet-ultra-deep-neural-networks-without
FractalNet: Ultra-Deep Neural Networks without Residuals