  Driven by the demand for energy-efficient employment of deep neural networks,
early-exit methods have experienced a notable increase in research attention.
These strategies allow for swift predictions by making decisions early in the
network, thereby conserving computation time and resources. However, so far the
early-exit networks have only been developed for stationary data distributions,
which restricts their application in real-world scenarios with continuous
non-stationary data. This study aims to explore the continual learning of the
early-exit networks. We adapt existing continual learning methods to fit with
early-exit architectures and investigate their behavior in the continual
setting. We notice that early network layers exhibit reduced forgetting and can
outperform standard networks even when using significantly fewer resources.
Furthermore, we analyze the impact of task-recency bias on early-exit inference
and propose Task-wise Logits Correction (TLC), a simple method that equalizes
this bias and improves the network performance for every given compute budget
in the class-incremental setting. We assess the accuracy and computational cost
of various continual learning techniques enhanced with early-exits and TLC
across standard class-incremental learning benchmarks such as 10 split CIFAR100
and ImageNetSubset and show that TLC can achieve the accuracy of the standard
methods using less than 70\% of their computations. Moreover, at full
computational budget, our method outperforms the accuracy of the standard
counterparts by up to 15 percentage points. Our research underscores the
inherent synergy between early-exit networks and continual learning,
emphasizing their practical utility in resource-constrained environments.
