Code used for the paper 'A Sparse Quantized Hopfield Network for Online-Continual Memory'.

Run with Python 3.7.6 and Pytorch 1.10.0.

All datasets besides the TinyImagenet dataset are downloaded automatically via Pytorch. To download TinyImagenet see https://github.com/tjmoon0104/pytorch-tiny-imagenet?tab=readme-ov-file

To reproduce data from a training run/experiment:

main.py --test argument

To reproduce plots for a training run/experiment:

main.py --plot argument

Here are the arguments for the various experiments used to reproduce tests and plots:

associative memory comparisons: assoc_comp

online-continual tests one hidden layer: OnCont-L1

online-continual tests three hidden layer: OnCont-L3

noisy encoding tests one hidden layer: nsEncode-L1

noisy encoding tests one hidden layer: nsEncode-L3

noisy encoding tests one hidden layer: recog

architecture comparisons: arch compare

For example, to reproduce the plots for the online-continual task with the one hidden layer models, run

main.py --test OnCont-L1

followed by

main.py --plot OnCont-L1