Swapout is a method that stochastically selects forward propagation in a neural network from a palette of choices: drop, identity, feedforward, residual. Achieves best results on CIFAR-10,100 that I'm aware of.

This paper examines a stochastic training method for deep architectures that is formulated in such a way that the method generalizes dropout and stochastic depth techniques. The paper studies a stochastic formulation for layer outputs which could be formulated as $Y =\Theta_1  \odot X+ \Theta_2  \odot F(X)$ where $\Theta_1$ and $\Theta_2$ are tensors of i.i.d. Bernoulli random variables. This allows layers to either: be dropped $(Y=0)$, act a feedforward layer $Y=F(X)$, be skipped $Y=X$, or behave like a residual network $Y=X+F(X)$. The paper provides some well reasoned conjectures as to why "both dropout and swapout networks interact poorly with batch normalization if one uses deterministic inference", while also providing some nice experiments on the importance of the choice of the form of stochastic training schedules and the number of samples required to obtain estimates that make sampling useful. The approach is able to yield performance improvement over comparable models if the key and critical details of the stochastic training schedule and a sufficient number of samples are used.

This paper proposes a generalization of some stochastic regularization techniques for effectively training deep networks with skip connections (i.e. dropout, stochastic depth, ResNets.) Like stochastic depth, swapout allows for connections that randomly skip layers, which has been shown to give improved performance--perhaps due to shorter paths to the loss layer and the resulting implicit ensemble over architectures with differing depth. However, like dropout, swapout is independently applied to each unit in a layer allowing for a richer space of sampled architectures. Since accurate expectation approximations are not easily attainable due to the skip connections, the authors propose stochastic inference (in which multiple forward passes are averaged during inference) instead of deterministic inference. To evaluate its effectiveness, the authors evaluate swapout on the CIFAR dataset, showing improvements over various baselines.