  Edge offloading for deep neural networks (DNNs) can be adaptive to the
input's complexity by using early-exit DNNs. These DNNs have side branches
throughout their architecture, allowing the inference to end earlier in the
edge. The branches estimate the accuracy for a given input. If this estimated
accuracy reaches a threshold, the inference ends on the edge. Otherwise, the
edge offloads the inference to the cloud to process the remaining DNN layers.
However, DNNs for image classification deals with distorted images, which
negatively impact the branches' estimated accuracy. Consequently, the edge
offloads more inferences to the cloud. This work introduces expert side
branches trained on a particular distortion type to improve robustness against
image distortion. The edge detects the distortion type and selects appropriate
expert branches to perform the inference. This approach increases the estimated
accuracy on the edge, improving the offloading decisions. We validate our
proposal in a realistic scenario, in which the edge offloads DNN inference to
Amazon EC2 instances.
