Continual Learning for Deep Dense Prediction
Transferring a deep learning model from old tasks to a new one is known to suffer from the catastrophic forgetting effects. Such forgetting mechanism is problematic as it does not allow us to accumulate knowledge sequentially and requires retaining and retraining on all the training data. Existing t...
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Virginia Tech
2018
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| Online Access: | http://hdl.handle.net/10919/83513 |