The study explores the benefits of early-exit networks in continual learning. It introduces Task-wise Logits Correction (TLC) to address task-recency bias, improving network performance. Results show that early-exit methods can outperform standard approaches with reduced computational resources.
The research highlights the synergy between early-exit networks and continual learning, emphasizing their practical utility in resource-constrained environments. By adapting existing methods to fit with early-exit architectures, the study showcases improved efficiency and accuracy. The proposed TLC method equalizes confidence levels between tasks, accelerating inference time while maintaining accuracy.
Key findings include the mitigation of catastrophic forgetting by early ICs, the impact of overthinking on network performance, and the detrimental effect of task-recency bias on dynamic inference. The study demonstrates that early-exit networks can achieve comparable or superior accuracy using significantly fewer computational resources.
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