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Continual Learning with Gated Incremental Memories for Sequential Data Processing. IJCNN 2020. Continual Learning with Recurrent Neural Networks (RNNs) inspired by Progressive network architecture.

License: GNU General Public License v3.0

Python 100.00%
continual-learning recurrent-neural-networks pytorch machine-learning rnn audioset

continuallearning-sequentialprocessing's Introduction

Hi there ๐Ÿ‘‹

I am an assistant professor (RTD-A in Italy) at University of Pisa and Board Member at ContinualAI.
My research focuses on Continual Learning, with applications to Recurrent Neural Networks models and sequential data processing.

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continuallearning-sequentialprocessing's Issues

Missing helper functions

Thanks for releasing the code for this interesting paper.
I tried to run the code, but it seems that multiple helper function are missing, e.g. in the mnist experiment:
write_test_results(...)
write_intermediate_test_results(...)
save_autoencoders(...)
etc.

DataLoader Error

python mnist.py generates following issue:

Traceback (most recent call last):
File "mnist.py", line 289, in
loader_task_test = DataLoader(mnist_dataset_test, batch_size=args.batch_size, shuffle=False, drop_last=True)

Remedy:
Add following line in mnist.py
from torch.utils.data import DataLoader

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