K Leuven, Matthias De Lange, Eli Verwimp
We lifted 10 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Mattdl/RehearsalRevealed | canonical | 5 of 9 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| ResNet18 | Ran | Mattdl/RehearsalRevealed/ridge_aversion_exp/model.py code served (permissive licence) · get_code("b03f148b0213ab66") |
| calc_full_grad_norm | Ran | Mattdl/RehearsalRevealed/grad_norm_exp/grad_norms.py code served (permissive licence) · get_code("5f3b1c7207af014b") |
| conv3x3 | Ran | Mattdl/RehearsalRevealed/ridge_aversion_exp/model.py code served (permissive licence) · get_code("583f9780bdd00a45") |
| get_split_cifar10 | Ran | Mattdl/RehearsalRevealed/framework/cole/core.py code served (permissive licence) · get_code("55bf3bcd2870630b") |
| pil_loader | Ran | this paper's copy was not recorded; identical code first harvested from neuroethology/bkind pointer only · get_code("f321f54723433661") |
| rescale_model_coords | Ran | Mattdl/RehearsalRevealed/contour_exp/mode_connectivity_plot.py code served (permissive licence) · get_code("3df54e3aaa048117") |
| get_single_label_mnist | Not yet run | Mattdl/RehearsalRevealed/framework/cole/core.py code served (permissive licence) · get_code("4ac54a7e815a9d58") |
| get_split_mini_imagenet | Not yet run | Mattdl/RehearsalRevealed/ridge_aversion_exp/miniimagenet.py code served (permissive licence) · get_code("31b613cae5ceb8b8") |
| get_split_mnist | Not yet run | Mattdl/RehearsalRevealed/framework/cole/core.py code served (permissive licence) · get_code("8e18f937db588ef5") |
| merge_paths | Not yet run | Mattdl/RehearsalRevealed/grad_norm_exp/grad_norms_plot.py code served (permissive licence) · get_code("d8513af311af3e75") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Learning from non-stationary data streams and overcoming catastrophic forgetting still poses a serious challenge for machine learning research. Rather than aiming to improve state-of-the-art, in this work we provide insight into the limits and merits of rehearsal, one of continual learning's most established methods. We hypothesize that models trained sequentially with rehearsal tend to stay in the same low-loss region after a task has finished, but are at risk of overfitting on its sample memory, hence harming generalization. We provide both conceptual and strong empirical evidence on three benchmarks for both behaviors, bringing novel insights into the dynamics of rehearsal and continual learning in general. Finally, we interpret important continual learning works in the light of our findings, allowing for a deeper understanding of their successes. 1 * Authors contributed equally.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2104.07446")
get_code_for_paper("2104.07446")
have("2104.07446")
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