Comments (3)
Joint training is indeed training on all the data at the same time, but this gives different results for the three continual learning scenarios because in each scenario the network must learn something else. For Split MNIST, the different mappings that the network is supposed to learn are illustrated in Figure 2 in the accompanying article (https://www.nature.com/articles/s42256-022-00568-3#Fig2). Hope this helps!
from continual-learning.
So, for joint training (task incremental and domain incremental), the output size is equal to 'within-context' label size (for above example, its 2) and for class incremental its the 'global-label' size which is 10 in the above case. Is my understanding correct?
from continual-learning.
For domain- and class-incremental learning that is correct. For task-incremental learning the output size is typically taken to be equal to the 'global-label' size, with the provided context label being used to set only those output units of classes in the current task to 'active' (i.e., to have a multi-head output layer).
from continual-learning.
Related Issues (20)
- Empirical Fisher Estimation HOT 3
- Datasets more complicated than MNIST HOT 1
- Just a request
- Grad in SI HOT 4
- Wrong dataset? HOT 2
- why batch_size has to be 1 when update fisher? HOT 1
- Lower/Upper Bound Experiments HOT 2
- one little confusion about the loss_fn_kd function HOT 1
- Suspicious Precision HOT 3
- Link error HOT 2
- Reproducing BI+SI method HOT 9
- about kafc fisher infromation matrix HOT 1
- How to create Resnet34 HOT 2
- Task-IL evaluation HOT 2
- Single head or multihead task incremental HOT 1
- 0 accuracy values for task-free setting HOT 9
- Whether context identity must be inferred in case of domain increment? HOT 1
- About printing results of experimental output
- Results for None ("lower target")
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from continual-learning.