Comments (1)
Fisher Information is taken as expectation over the variance of score per sample. PyTorch currently doesn't support batch-computation of gradients per sample, hence the batch size of 1.
You can compute batch gradients per sample, by batching forward pass, but disabling loss reduction to mean and backward()
each loss value of the batch.
Conversely, limit the number of samples used to compute Fisher (say 256), which is good for most use-cases.
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
- 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
- Joint training results different for different types of incremental learning? HOT 3
- 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.