Datasets:

jonpark0
/

contilab-cpu-study

---
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.

	---
	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.