Functional Regularisation for Continual Learning with Gaussian Processes
by Pavel Andreev, Peter Mokrov and Alexander Kagan
This is an unofficial PyTorch implementation of the paper https://arxiv.org/abs/1901.11356 . The main goal of this project is to provide an independent reproduction of the results presented in the paper.
Project Proposal: pdf
To launch our experiments use results_script.py
The example of script run below:
> python .\results_script.py --device 'your device' --task 'permuted_mnist' --method 'baseline' --n_inducing 2Available options for --task argument are split_mnist, permuted_mnist and omniglot.
Available options for --method argument are baseline, frcl_random and frcl_trace.
Results of our experiments are presented in '.\results'. Besides, one can find notebooks with minimal working examples in '.\notebooks'.
The presentation with the project main results is available here.
We results are also summarized in the table below.
| Datset | Method | N points | Criteria | Accuracy (ours) | Accuracy (paper) |
|---|---|---|---|---|---|
| Split-MNIST | baseline | 2 | - | 0.981 | - |
| Split-MNIST | baseline | 40 | - | 0.985 | 0.958 |
| Split-MNIST | FRCL | 2 | Random | 0.827 | 0.598 |
| Split-MNIST | FRCL | 2 | Trace | 0.82 | 0.82 |
| Split-MNIST | FRCL | 40 | Random | 0.986 | 0.971 |
| Split-MNIST | FRCL | 40 | Trace | 0.979 | 0.978 |
| Permuted-MNIST | baseline | 10 | - | 0.695 | 0.486 |
| Permuted-MNIST | baseline | 80 | - | 0.865 | - |
| Permuted-MNIST | baseline | 200 | - | 0.908 | 0.823 |
| Permuted-MNIST | FRCL | 10 | Random | 0.628/0.527* | 0.482 |
| Permuted-MNIST | FRCL | 80 | Random | 0.838 | - |
| Permuted-MNIST | FRCL | 200 | Random | 0.942 | 0.943 |
| Omniglot-10 | baseline | 60 | - | 0.381 | - |
| Omniglot-10 | FRCL | 60 | Random | 0.376 | - |
Results of our experiments are presented in .\results
* the results appeared to significantly depend on initialization of parameters
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