Datasets:
Formats:
csv
Size:
< 1K
Tags:
continual-learning
lifelong-learning
catastrophic-forgetting
class-incremental-learning
benchmark
model-merging
License:
File size: 4,987 Bytes
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license: mit
pretty_name: "contilab CPU study: continual learning results"
tags:
- continual-learning
- lifelong-learning
- catastrophic-forgetting
- class-incremental-learning
- benchmark
- model-merging
- knowledge-distillation
size_categories:
- n<1K
configs:
- config_name: cpu_main
data_files: cpu_main.csv
default: true
- config_name: cpu_tuned
data_files: cpu_tuned.csv
- config_name: cpu_student
data_files: cpu_student.csv
---
# contilab CPU study: continual learning results
Per-run results of an empirical comparison of **12 continual-learning methods on 6 benchmarks**, produced with
[contilab](https://github.com/JonPark0/contilab) 0.3.1, a PyTorch framework for continual learning research. One row per run, with every
scalar metric: final accuracy, forgetting, backward and forward transfer, the stability gap, task-recency bias,
calibration, representation metrics, memory and wall time.
[Results page](https://jonpark0.github.io/contilab/wiki/Results.html) (rankings, significance tests, findings) · [Docs](https://jonpark0.github.io/contilab/) ·
[GitHub](https://github.com/JonPark0/contilab) · DOI [10.5281/zenodo.23906803](https://doi.org/10.5281/zenodo.23906803)
## Files
| File | Config | Rows | Contents | Experiment config |
|---|---|---|---|---|
| `cpu_main.csv` | `cpu_main` | 324 | Main study: every method with default hyperparameters on 6 benchmarks | `cpu_main.yaml` + `cpu_main_seeds.yaml` |
| `cpu_tuned.csv` | `cpu_tuned` | 456 | Tuned comparison: per-method grids on the two MNIST benchmarks, variant chosen on validation accuracy (`selected`) | `cpu_tuned.yaml` |
| `cpu_student.csv` | `cpu_student` | 12 | Teacher → smaller student: distillation into a 2×100 MLP | `cpu_student.yaml` |
The main study has 324 runs: 312 done, 12 diverged, 0 failed.
Seeds per benchmark: Split MNIST (Class-IL) 6, Split MNIST (Task-IL) 6, Permuted MNIST (Domain-IL) 6, Split CIFAR-10 (Class-IL) 3, Split CIFAR-100 (Class-IL) 3, text5 + Pythia-70M (Class-IL) 3.
```python
from datasets import load_dataset
main = load_dataset("jonpark0/contilab-cpu-study", "cpu_main", split="train")
```
## Columns
- **Identifiers:** `run_id`, `preset` (benchmark and scenario), `method`, `variant` (swept hyperparameters),
`seed`, `status` (`done`, `diverged` or `failed`). `cpu_tuned` adds `selected` (the variant chosen on validation
accuracy) and `cell` (the comparison-axis part of the variant).
- **Metrics:** every other column. Definitions and references are on the [Metrics](https://jonpark0.github.io/contilab/wiki/Metrics.html)
page. An empty cell means the metric is undefined for that run, for example forward transfer in Class-IL, stream
metrics for consolidation methods, or anything for a diverged run.
## Final accuracy (cpu_main, mean over seeds)
| Method | Split MNIST (Class-IL) | Split MNIST (Task-IL) | Permuted MNIST (Domain-IL) | Split CIFAR-10 (Class-IL) | Split CIFAR-100 (Class-IL) | text5 + Pythia-70M (Class-IL) |
|---|---|---|---|---|---|---|
| Naive | 0.196 | 0.980 | 0.805 | 0.181 | 0.049 | 0.097 |
| Joint (upper bound) | 0.950 | 0.993 | 0.909 | 0.398 | 0.118 | 0.529 |
| EWC | 0.197 | 0.983 | diverged | diverged | diverged | 0.105 |
| LwF | 0.228 | 0.990 | 0.817 | 0.173 | 0.047 | 0.105 |
| Experience Replay | 0.858 | 0.983 | 0.869 | 0.302 | 0.059 | 0.329 |
| DER++ | 0.871 | 0.991 | 0.902 | 0.256 | 0.059 | 0.397 |
| A-GEM | 0.335 | 0.984 | 0.864 | 0.181 | 0.048 | 0.100 |
| LoRA-per-task | 0.184 | 0.986 | 0.619 | 0.137 | 0.015 | 0.024 |
| Task Arithmetic | 0.531 | 0.979 | 0.570 | 0.158 | 0.011 | 0.034 |
| TIES-Merging | 0.561 | 0.983 | 0.677 | 0.166 | 0.013 | 0.033 |
| Multi-teacher distillation | 0.863 | 0.986 | 0.773 | 0.262 | 0.040 | 0.452 |
| Continual distillation | 0.586 | 0.980 | 0.818 | 0.210 | 0.030 | 0.331 |
"diverged" marks a benchmark where every seed of the method diverged (its loss stopped being finite); such runs
are kept with `status = diverged`.
## How the data were produced
- Hardware: 4 CPU cores (Intel Xeon @ 2.1 GHz), no GPU.
- Vision: MLP 2×400 on the MNIST benchmarks (1 epoch per task), reduced ResNet-18 on Split CIFAR-10/100
(2 epochs). Language: Pythia-70M on text5 (AG News → Yelp → Amazon → DBpedia → Yahoo Answers), 1,000 training
texts per task.
- Replay and distillation methods keep 500 samples (vision) or 200 (text).
- Reproduce with `contilab run configs/experiments/<config>.yaml`; the setup and every deviation from the design
are listed on the [Results](https://jonpark0.github.io/contilab/wiki/Results.html) page.
## Citation
```bibtex
@software{contilab2026,
author = {Park, Yeochoon},
title = {contilab: a performance-first framework for continual learning benchmarking, distillation and model merging},
year = {2026},
version = {0.3.1},
doi = {10.5281/zenodo.23906803},
url = {https://github.com/JonPark0/contilab}
}
```
## License
MIT, like contilab. The benchmarks' source datasets keep their own licenses.
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