Unsupervised Early Exit in DNNs with Multiple Exits. (arXiv:2209.09480v1 [cs.LG])

Deep Neural Networks (DNNs) are generally designed as sequentially cascaded
differentiable blocks/layers with a prediction module connected only to its
last layer. DNNs can be attached with prediction modules at multiple points
along the backbone where inference can stop at an intermediary stage without
passing through all the modules. The last exit point may offer a better
prediction error but also involves more computational resources and latency. An
exit point that is `optimal' in terms of both prediction error and …

arxiv exit unsupervised