Comments (2)
Hi,
Do you mean while training? Do you mean after smoothing? In the simple case the importance is proportional to the loss. If you want to compute the importance of each sample in the dataset it is simply the loss value of each sample.
If you look into the implementations we have developed more importance metrics (not yet in the paper).
What are you trying to achieve exactly?
from importance-sampling.
Closing because of inactivity.
from importance-sampling.
Related Issues (20)
- Addition of RAIS in the implemented methods
- The result seems okay, but there are a few confusing things HOT 2
- Fail when metric given as function HOT 9
- About the complete importance sampling code HOT 9
- Add initial_epoch to ImportanceTraining classes
- Remove unnecessary loading of validation data to memory HOT 2
- Failed when using ModelCheckpoint callback HOT 3
- Port to tensorflow 2 HOT 20
- Support of networks with multiple outputs HOT 10
- How do I get the serial Numbers of important samples HOT 1
- High GPU memory usage HOT 4
- Memory leak? HOT 2
- Question regarding Eq. 29 HOT 1
- Question regarding maximum variance reduction & speedup HOT 4
- RuntimeError: The layer has never been called and thus has no defined output shape. HOT 6
- Adjust Learning rate
- Compatibility with tensorflow = 2.5.0 and Keras = 2.4 HOT 4
- tensorflow and keras versions HOT 2
- Some confusion about the fast grads calculation when converting to Pytorch. HOT 3
- Would it be possible to calculate b from tau_threshold?
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from importance-sampling.