# In Defense of the Learning Without Forgetting for Task Incremental Learning

arXiv: https://arxiv.org/abs/2107.12304 · HTML: https://mlsanity.com/paper/2107.12304/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 26 Jul 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [PathNet: Evolution Channels Gradient Descent in Super Neural Networks](https://mlsanity.com/paper/1701.08734/) (2017)
- [Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting](https://mlsanity.com/paper/1904.00310/) (2019)
- [Continual learning with hypernetworks](https://mlsanity.com/paper/1906.00695/) (2019)
- [Overcoming Catastrophic Forgetting by Incremental Moment Matching](https://mlsanity.com/paper/1703.08475/) (2017)
- [Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines](https://mlsanity.com/paper/1810.12488/) (2018)
- [Adversarial Continual Learning](https://mlsanity.com/paper/2003.09553/) (2020)
- [A continual learning survey: Defying forgetting in classification tasks](https://mlsanity.com/paper/1909.08383/) (2019)
- [Encoder Based Lifelong Learning](https://mlsanity.com/paper/1704.01920/) (2017)
- [Implicit Weight Uncertainty in Neural Networks](https://mlsanity.com/paper/1711.01297/) (2017)

## Built on by (most cited first, 13 of 13)

- [Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters](https://mlsanity.com/paper/2403.11549/) (2024)
- [Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning](https://mlsanity.com/paper/2202.10203/) (2022)
- [NPCL: Neural Processes for Uncertainty-Aware Continual Learning](https://mlsanity.com/paper/2310.19272/) (2023)
- [Componential Prompt-Knowledge Alignment for Domain Incremental Learning](https://mlsanity.com/paper/2505.04575/) (2025)
- [Forgetting, Ignorance or Myopia: Revisiting Key Challenges in Online Continual Learning](https://mlsanity.com/paper/2409.19245/) (2024)
- [Continual Learning with Guarantees via Weight Interval Constraints](https://mlsanity.com/paper/2206.07996/) (2022)
- [Unforgotten Safety: Preserving Safety Alignment of Large Language Models with Continual Learning](https://mlsanity.com/paper/2512.10150/) (2025)
- [Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI](https://mlsanity.com/paper/2205.01929/) (2022)
- [Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory](https://mlsanity.com/paper/2405.17457/) (2024)
- [Contrastive Regularization over LoRA for Multimodal Biomedical Image Incremental Learning](https://mlsanity.com/paper/2508.11673/) (2025)
- [Fresh-CL: Feature Realignment through Experts on Hypersphere in Continual Learning](https://mlsanity.com/paper/2501.02198/) (2025)
- [ContiGuard: A Framework for Continual Toxicity Detection Against Evolving Evasive Perturbations](https://mlsanity.com/paper/2603.14843/) (2026)
- [CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments](https://mlsanity.com/paper/2603.17548/) (2026)

## Abstract (arXiv)

Catastrophic forgetting is one of the major challenges on the road for continual learning systems, which are presented with an on-line stream of tasks. The field has attracted considerable interest and a diverse set of methods have been presented for overcoming this challenge. Learning without Forgetting (LwF) is one of the earliest and most frequently cited methods. It has the advantages of not requiring the storage of samples from the previous tasks, of implementation simplicity, and of being well-grounded by relying on knowledge distillation. However, the prevailing view is that while it shows a relatively small amount of forgetting when only two tasks are introduced, it fails to scale to long sequences of tasks. This paper challenges this view, by showing that using the right architecture along with a standard set of augmentations, the results obtained by LwF surpass the latest algorithms for task incremental scenario. This improved performance is demonstrated by an extensive set of experiments over CIFAR-100 and Tiny-ImageNet, where it is also shown that other methods cannot benefit as much from similar improvements.