# Online Continual Learning Via Candidates Voting

arXiv: https://arxiv.org/abs/2110.08855 · HTML: https://mlsanity.com/paper/2110.08855/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 45 references that are in the map.
- First posted on arXiv 17 Oct 2021.

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

- [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)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [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 Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [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)
- [CORe50: a New Dataset and Benchmark for Continuous Object Recognition](https://mlsanity.com/paper/1705.03550/) (2017)
- [Maintaining Discrimination and Fairness in Class Incremental Learning](https://mlsanity.com/paper/1911.07053/) (2019)
- [Online Continual Learning in Image Classification: An Empirical Survey](https://mlsanity.com/paper/2101.10423/) (2021)
- [Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines](https://mlsanity.com/paper/1810.12488/) (2018)
- [Mnemonics Training: Multi-Class Incremental Learning without Forgetting](https://mlsanity.com/paper/2002.10211/) (2020)
- [FearNet: Brain-Inspired Model for Incremental Learning](https://mlsanity.com/paper/1711.10563/) (2017)
- [Using Hindsight to Anchor Past Knowledge in Continual Learning](https://mlsanity.com/paper/2002.08165/) (2020)
- [Dataset Augmentation in Feature Space](https://mlsanity.com/paper/1702.05538/) (2017)
- [Conditional Channel Gated Networks for Task-Aware Continual Learning](https://mlsanity.com/paper/2004.00070/) (2020)
- [Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild](https://mlsanity.com/paper/1903.12648/) (2019)
- [Continuous Learning in Single-Incremental-Task Scenarios](https://mlsanity.com/paper/1806.08568/) (2018)
- [Less-forgetting Learning in Deep Neural Networks](https://mlsanity.com/paper/1607.00122/) (2016)
- [Incremental Learning In Online Scenario](https://mlsanity.com/paper/2003.13191/) (2020)
- [Online Continual Learning For Visual Food Classification](https://mlsanity.com/paper/2108.06781/) (2021)
- [Efficient Continual Learning in Neural Networks with Embedding Regularization](https://mlsanity.com/paper/1909.03742/) (2019)
- [Unsupervised Continual Learning Via Pseudo Labels](https://mlsanity.com/paper/2104.07164/) (2021)
- [A Strategy for an Uncompromising Incremental Learner](https://mlsanity.com/paper/1705.00744/) (2017)

## Built on by (most cited first, 15 of 15)

- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Gradient Reweighting: Towards Imbalanced Class-Incremental Learning](https://mlsanity.com/paper/2402.18528/) (2024)
- [Exemplar-free Online Continual Learning](https://mlsanity.com/paper/2202.05491/) (2022)
- [Rethinking Momentum Knowledge Distillation in Online Continual Learning](https://mlsanity.com/paper/2309.02870/) (2023)
- [Out-Of-Distribution Detection In Unsupervised Continual Learning](https://mlsanity.com/paper/2204.05462/) (2022)
- [Hierarchically Structured Task-Agnostic Continual Learning](https://mlsanity.com/paper/2211.07725/) (2022)
- [Mixture-of-Variational-Experts for Continual Learning](https://mlsanity.com/paper/2110.12667/) (2021)
- [DELTA: Decoupling Long-Tailed Online Continual Learning](https://mlsanity.com/paper/2404.04476/) (2024)
- [Learning Representations on the Unit Sphere: Investigating Angular Gaussian and von Mises-Fisher Distributions for Online Continual Learning](https://mlsanity.com/paper/2306.03364/) (2023)
- [Personalized Food Image Classification: Benchmark Datasets and New Baseline](https://mlsanity.com/paper/2309.08744/) (2023)
- [Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning](https://mlsanity.com/paper/2207.12831/) (2022)
- [Learning to Classify New Foods Incrementally Via Compressed Exemplars](https://mlsanity.com/paper/2404.07507/) (2024)
- [I2CANSAY:Inter-Class Analogical Augmentation and Intra-Class Significance Analysis for Non-Exemplar Online Task-Free Continual Learning](https://mlsanity.com/paper/2404.13576/) (2024)
- [Rehearsal-free and Task-free Online Continual Learning With Contrastive Prompt](https://mlsanity.com/paper/2510.00467/) (2025)
- [From Offline to Online Memory-Free and Task-Free Continual Learning via Fine-Grained Hypergradients](https://mlsanity.com/paper/2502.18762/) (2025)

## Abstract (arXiv)

Continual learning in online scenario aims to learn a sequence of new tasks from data stream using each data only once for training, which is more realistic than in offline mode assuming data from new task are all available. However, this problem is still under-explored for the challenging class-incremental setting in which the model classifies all classes seen so far during inference. Particularly, performance struggles with increased number of tasks or additional classes to learn for each task. In addition, most existing methods require storing original data as exemplars for knowledge replay, which may not be feasible for certain applications with limited memory budget or privacy concerns. In this work, we introduce an effective and memory-efficient method for online continual learning under class-incremental setting through candidates selection from each learned task together with prior incorporation using stored feature embeddings instead of original data as exemplars. Our proposed method implemented for image classification task achieves the best results under different benchmark datasets for online continual learning including CIFAR-10, CIFAR-100 and CORE-50 while requiring much less memory resource compared with existing works.