# Task-Free Continual Learning via Online Discrepancy Distance Learning

arXiv: https://arxiv.org/abs/2210.06579 · HTML: https://mlsanity.com/paper/2210.06579/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 63 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 30 of 41)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [Gradient based sample selection for online continual learning](https://mlsanity.com/paper/1903.08671/) (2019)
- [Online Continual Learning with Maximally Interfered Retrieval](https://mlsanity.com/paper/1908.04742/) (2019)
- [Variational Continual Learning](https://mlsanity.com/paper/1710.10628/) (2017)
- [Bridging Theory and Algorithm for Domain Adaptation](https://mlsanity.com/paper/1904.05801/) (2019)
- [Rainbow Memory: Continual Learning with a Memory of Diverse Samples](https://mlsanity.com/paper/2103.17230/) (2021)
- [Task-Free Continual Learning](https://mlsanity.com/paper/1812.03596/) (2018)
- [Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting](https://mlsanity.com/paper/1805.07810/) (2018)
- [Continual Unsupervised Representation Learning](https://mlsanity.com/paper/1910.14481/) (2019)
- [Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams](https://mlsanity.com/paper/2009.00919/) (2020)
- [A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning](https://mlsanity.com/paper/2001.00689/) (2020)
- [Online Coreset Selection for Rehearsal-based Continual Learning](https://mlsanity.com/paper/2106.01085/) (2021)
- [A Survey of Deep Learning Applications to Autonomous Vehicle Control](https://mlsanity.com/paper/1912.10773/) (2019)
- [Less-forgetting Learning in Deep Neural Networks](https://mlsanity.com/paper/1607.00122/) (2016)
- [Representational Continuity for Unsupervised Continual Learning](https://mlsanity.com/paper/2110.06976/) (2021)
- [Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning](https://mlsanity.com/paper/2112.02706/) (2021)
- [Continual Learning in the Teacher-Student Setup: Impact of Task Similarity](https://mlsanity.com/paper/2107.04384/) (2021)
- [Optimal Continual Learning has Perfect Memory and is NP-hard](https://mlsanity.com/paper/2006.05188/) (2020)
- [TRGP: Trust Region Gradient Projection for Continual Learning](https://mlsanity.com/paper/2202.02931/) (2022)
- [Gradient-based Editing of Memory Examples for Online Task-free Continual Learning](https://mlsanity.com/paper/2006.15294/) (2020)
- [Information-theoretic Online Memory Selection for Continual Learning](https://mlsanity.com/paper/2204.04763/) (2022)

## Built on by (most cited first, 22 of 22)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning](https://mlsanity.com/paper/2307.09218/) (2023)
- [Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning](https://mlsanity.com/paper/2403.18886/) (2024)
- [The Ideal Continual Learner: An Agent That Never Forgets](https://mlsanity.com/paper/2305.00316/) (2023)
- [Online Continual Learning For Interactive Instruction Following Agents](https://mlsanity.com/paper/2403.07548/) (2024)
- [CorpusBrain++: A Continual Generative Pre-Training Framework for Knowledge-Intensive Language Tasks](https://mlsanity.com/paper/2402.16767/) (2024)
- [Clustering-based Domain-Incremental Learning](https://mlsanity.com/paper/2309.12078/) (2023)
- [Statistical Context Detection for Deep Lifelong Reinforcement Learning](https://mlsanity.com/paper/2405.19047/) (2024)
- [Doubly Perturbed Task Free Continual Learning](https://mlsanity.com/paper/2312.13027/) (2023)
- [Overcoming Catastrophic Forgetting in Tabular Data Classification: A Pseudorehearsal-based approach](https://mlsanity.com/paper/2407.09039/) (2024)
- [PrePrompt: Predictive prompting for class incremental learning](https://mlsanity.com/paper/2505.08586/) (2025)
- [More Experts Than Galaxies: Conditionally-overlapping Experts With Biologically-Inspired Fixed Routing](https://mlsanity.com/paper/2410.08003/) (2024)
- [Improvements of Dark Experience Replay and Reservoir Sampling towards Better Balance between Consolidation and Plasticity](https://mlsanity.com/paper/2504.20932/) (2025)
- [The Bayesian Approach to Continual Learning: An Overview](https://mlsanity.com/paper/2507.08922/) (2025)
- [Unlearning-based sliding window for continual learning under concept drift](https://mlsanity.com/paper/2603.14484/) (2026)
- [Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights](https://mlsanity.com/paper/2604.17578/) (2026)
- [OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning](https://mlsanity.com/paper/2310.03848/) (2023)
- [Task agnostic continual learning with Pairwise layer architecture](https://mlsanity.com/paper/2405.13632/) (2024)
- [Reliably Detecting Model Failures in Deployment Without Labels](https://mlsanity.com/paper/2506.05047/) (2025)
- [Continual Learning for non-stationary regression via Memory-Efficient Replay](https://mlsanity.com/paper/2602.09720/) (2026)
- [Unsupervised Continual Learning with Growing Self-Organizing Maps and Synthetic Replay](https://mlsanity.com/paper/2608.27662/) (2026)
- [Neural-Collapse-guided Task-Free Continual Anomaly Detection](https://mlsanity.com/paper/2609.03406/) (2026)

## Abstract (arXiv)

Learning from non-stationary data streams, also called Task-Free Continual Learning (TFCL) remains challenging due to the absence of explicit task information. Although recently some methods have been proposed for TFCL, they lack theoretical guarantees. Moreover, forgetting analysis during TFCL was not studied theoretically before. This paper develops a new theoretical analysis framework which provides generalization bounds based on the discrepancy distance between the visited samples and the entire information made available for training the model. This analysis gives new insights into the forgetting behaviour in classification tasks. Inspired by this theoretical model, we propose a new approach enabled by the dynamic component expansion mechanism for a mixture model, namely the Online Discrepancy Distance Learning (ODDL). ODDL estimates the discrepancy between the probabilistic representation of the current memory buffer and the already accumulated knowledge and uses it as the expansion signal to ensure a compact network architecture with optimal performance. We then propose a new sample selection approach that selectively stores the most relevant samples into the memory buffer through the discrepancy-based measure, further improving the performance. We perform several TFCL experiments with the proposed methodology, which demonstrate that the proposed approach achieves the state of the art performance.