Continual learning consists of algorithms that learn from a stream of data/tasks continuously and adaptively thought time, enabling the incremental development of ever more complex knowledge and skills. The lack of consensus in evaluating continual learning algorithms and the almost exclusive focus on forgetting motivate us to propose a more comprehensive set of implementation independent metrics accounting for several factors we believe have practical implications worth considering in the deployment of real AI systems that learn continually: accuracy or performance over time, backward and forward knowledge transfer, memory overhead as well as computational efficiency. Drawing inspiration from the standard Multi-Attribute Value Theory (MAVT) we further propose to fuse these metrics into a single score for ranking purposes and we evaluate our proposal with five continual learning strategies on the iCIFAR-100 continual learning benchmark.

本文提出了一种更综合全面的衡量连续学习算法的方法，包括准确性、知识转移、内存占用和计算效率等多方面的度量，并将这些度量融合成一个评分，通过评估五种算法在iCIFAR-100数据集上的表现来证明这种方法的优越性。

不要忘记，遗忘并不是全部：连续学习的新指标