---
title: "Progress & Compress: A scalable framework for continual learning"
description: "Neural networks that learn tasks one after another tend to lose earlier skills, a problem called… 42 references, cited by 66 papers here."
authors: ["Jonathan Schwarz", "Jelena Luketina", "Wojciech M. Czarnecki", "Agnieszka Grabska-Barwinska", "Yee Whye Teh", "Razvan Pascanu", "Raia Hadsell"]
year: 2018
venue: "arXiv:1805.06370"
source: "https://arxiv.org/abs/1805.06370"
doi: "10.48550/arxiv.1805.06370"
arxiv: "1805.06370"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/schwarz-2018-progress-compress-scalable-framework-continual/"
---
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# Progress & Compress: A scalable framework for continual learning

**Authors:** [Jonathan Schwarz](https://md.research.elamur.ai/free/en/authors/jonathan-schwarz/), [Jelena Luketina](https://md.research.elamur.ai/free/en/authors/jelena-luketina/), [Wojciech M. Czarnecki](https://md.research.elamur.ai/free/en/authors/wojciech-marian-czarnecki/), Agnieszka Grabska-Barwinska, [Yee Whye Teh](https://md.research.elamur.ai/free/en/authors/yee-whye-teh/), [Razvan Pascanu](https://md.research.elamur.ai/free/en/authors/razvan-pascanu/) and Raia Hadsell · **Year:** 2018 · **Venue:** arXiv:1805.06370 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1805.06370) · [PDF](https://arxiv.org/pdf/1805.06370) · [DOI 10.48550/arxiv.1805.06370](https://doi.org/10.48550/arxiv.1805.06370) · [arXiv 1805.06370](https://arxiv.org/abs/1805.06370) · [OpenAlex](https://openalex.org/W2804175194)

**Citations:** 272 citations (OpenAlex, 2026-10-14) · [66](https://md.research.elamur.ai/free/en/papers/schwarz-2018-progress-compress-scalable-framework-continual/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Neural networks that learn tasks one after another tend to lose earlier skills, a problem called catastrophic forgetting. Existing fixes each have a cost. Finetuning forgets. EWC can become too rigid to learn new tasks. Progressive Networks grow quadratically in size as tasks accumulate. Schwarz et al. (DeepMind/Oxford, ICML 2018) propose Progress & Compress (P&C), which uses two fixed-size columns. In the "progress" phase, an active column learns the new task without restrictions and reuses features from a frozen knowledge base through lateral adaptor connections. In the "compress" phase, the active column's behaviour is distilled into the knowledge base. A new online EWC variant protects older skills during this step, and its penalty does not grow with the number of tasks. The method needs no stored data, no task-specific parameters and no growing architecture. The authors test it on sequential Omniglot alphabet classification, Atari games and 3D maze navigation. P&C achieves the best accuracy among the scalable methods while keeping the parameter count small. This makes continual learning feasible for long task sequences.

## Key points
- **Omniglot results after 5 passes:** P&C reaches 82.84 ± 1.4 test accuracy. This beats online EWC (79.15), EWC (75.96), LwF (66.48) and finetuning (60.74). The less scalable baselines score higher: Progressive Nets reach 86.50 and one model per task reaches 88.34.
- **Model size on Omniglot:**
  - P&C uses 659K parameters.
  - Progressive Nets use 108,000K.
  - Separate per-task models use 5,680K.
  - Original EWC uses 11,100K, because its regularisers accumulate.
- **Atari setup:** six games (Space Invaders, Krull, Beamrider, Hero, Stargunner, Ms. Pac-man) are each visited 5 times, with 50m frames per visit. P&C clearly improves on several games and roughly matches EWC baselines on the others.
- **Adaptors:** these are small MLPs, implemented as 1×1 convolutions in CNNs, with trainable scaling vectors. Distillation minimises the KL divergence between the active column's outputs and the knowledge base's outputs.
- **When P&C helps most:** it gains most where tasks allow positive transfer. On Omniglot, online EWC alone is already competitive.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Oxford](https://md.research.elamur.ai/free/en/universities/university-of-oxford/) — University, Oxford, United Kingdom · 122 works · 160,277 citations
- [Google DeepMind](https://md.research.elamur.ai/free/en/companies/google-deepmind/) — Company, London, United Kingdom · 72 works · 84,326 citations

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## Cite

Cite as: Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu and Raia Hadsell. 2018. Progress & Compress: A scalable framework for continual learning. arXiv:1805.06370.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{schwarz2018progress,
  title = {{Progress \& Compress: A scalable framework for continual learning}},
  author = {Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu and Raia Hadsell},
  year = {2018},
  doi = {10.48550/arxiv.1805.06370},
  eprint = {1805.06370},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1805.06370}
}
```

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*Updated 2 November 2026*