GMvandeVen / continual-learning

PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.

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Single head or multihead task incremental

COD1995 opened this issue · comments

Hi, there

Is this experiment on task incremental single head or multi head?

Thanks

Hi, in the accompanying article, most experiments in the task-incremental learning scenario use a multi-headed output layer. Exception are experiments in Supplemantary Note 7, in which there are experiments in the task-incremental learning scenario that use a single-headed output layer.
In the code, when you select the task-incremental learning scenario (using --scenario=task), by default a multi-headed output layer will be used, but in this scenario you can instead use a single-headed output layer by additionally using the option --singlehead.
Note that experiments in the domain- or class-incremental learning scenario always use a single-headed output layer.
Hope this helps!