We characterize a prevalent weakness of deep neural networks
(DNNs)---overthinking---which occurs when a DNN can reach correct predictions
before its final layer. Overthinking is computationally wasteful, and it can
also be destructive when, by the final layer, a correct prediction changes into
a misclassification. Understanding overthinking requires studying how each
prediction evolves during a DNN's forward pass, which conventionally is opaque.
For prediction transparency, we propose the Shallow-Deep Network (SDN), a
generic modification to off-the-shelf DNNs that introduces internal
classifiers. We apply SDN to four modern architectures, trained on three image
classification tasks, to characterize the overthinking problem. We show that
SDNs can mitigate the wasteful effect of overthinking with confidence-based
early exits, which reduce the average inference cost by more than 50% and
preserve the accuracy. We also find that the destructive effect occurs for 50%
of misclassifications on natural inputs and that it can be induced,
adversarially, with a recent backdooring attack. To mitigate this effect, we
propose a new confusion metric to quantify the internal disagreements that will
likely lead to misclassifications.

本研究发现深度神经网络中的过度思考现象及其带来的计算浪费和错误分类问题，提出了增加内部分类器的 Shallow-Deep Network 对于内部分类的可见性，同时通过引入基于置信度的早期决策来减少计算浪费并避免了 50％的自然输入误分类问题，并提出新的混淆度量方法来量化导致误分类的内部分歧。