Recommendation of data-free class-incremental learning algorithms by
  simulating future data
Eva Feillet Adrian Popescu Céline Hudelot
Class-incremental learning deals with sequential data streams composed of
batches of classes. Various algorithms have been proposed to address the
challenging case where samples from past classes cannot be stored. However,
selecting an appropriate algorithm for a user-defined setting is an open
problem, as the relative performance of these algorithms depends on the
incremental settings. To solve this problem, we introduce an algorithm
recommendation method that simulates the future data stream. Given an initial
set of classes, it leverages generative models to simulate future classes from
the same visual domain. We evaluate recent algorithms on the simulated stream
and recommend the one which performs best in the user-defined incremental
setting. We illustrate the effectiveness of our method on three large datasets
using six algorithms and six incremental settings. Our method outperforms
competitive baselines, and performance is close to that of an oracle choosing
the best algorithm in each setting. This work contributes to facilitate the
practical deployment of incremental learning.