  Incremental learning enables artificial agents to learn from sequential data.
While important progress was made by exploiting deep neural networks,
incremental learning remains very challenging. This is particularly the case
when no memory of past data is allowed and catastrophic forgetting has a strong
negative effect. We tackle class-incremental learning without memory by
adapting prediction bias correction, a method which makes predictions of past
and new classes more comparable. It was proposed when a memory is allowed and
cannot be directly used without memory, since samples of past classes are
required. We introduce a two-step learning process which allows the transfer of
bias correction parameters between reference and target datasets. Bias
correction is first optimized offline on reference datasets which have an
associated validation memory. The obtained correction parameters are then
transferred to target datasets, for which no memory is available. The second
contribution is to introduce a finer modeling of bias correction by learning
its parameters per incremental state instead of the usual past vs. new class
modeling. The proposed dataset knowledge transfer is applicable to any
incremental method which works without memory. We test its effectiveness by
applying it to four existing methods. Evaluation with four target datasets and
different configurations shows consistent improvement, with practically no
computational and memory overhead.
