Comments (1)
To minimize catastrophic forgetting, it's better not to pull down the previously learned distributions, even if they are not present in the training data.
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Related Issues (19)
- a few question about the implementation HOT 8
- Nan Loss during training MNIST dataset HOT 2
- Something strange in the Algorithm 1.. HOT 4
- Something strange about the update of theta and psi in the inner loop HOT 6
- What is the difference between two output of model? -->outputs2, outputs = model(inputs) HOT 1
- Training script for ImageNet-100 HOT 2
- Running without CUDA HOT 2
- Code for other methods
- Obviously catastrophic forgetting HOT 3
- AttributeError: type object 'args' has no attribute 'overflow' HOT 2
- A question about theta and task-specific phi HOT 1
- something wrong happends in training cifar10 HOT 3
- Where to check task accuracy and class accuracy? HOT 2
- Fair comparison HOT 2
- sys.argv[1] is out of range HOT 2
- train_mnist loss go to nan at Sess 3
- about adding parameters HOT 5
- about adding classes HOT 2
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