# Non-exemplar Class-incremental Learning by Random Auxiliary Classes Augmentation and Mixed Features

arXiv: https://arxiv.org/abs/2304.07707 · HTML: https://mlsanity.com/paper/2304.07707/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 59 references that are in the map.
- First posted on arXiv 16 Apr 2023.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [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)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [Class-incremental learning: survey and performance evaluation on image classification](https://mlsanity.com/paper/2010.15277/) (2020)
- [DER: Dynamically Expandable Representation for Class Incremental Learning](https://mlsanity.com/paper/2103.16788/) (2021)
- [Maintaining Discrimination and Fairness in Class Incremental Learning](https://mlsanity.com/paper/1911.07053/) (2019)
- [Learning without Memorizing](https://mlsanity.com/paper/1811.08051/) (2018)
- [FOSTER: Feature Boosting and Compression for Class-Incremental Learning](https://mlsanity.com/paper/2204.04662/) (2022)
- [Compacting, Picking and Growing for Unforgetting Continual Learning](https://mlsanity.com/paper/1910.06562/) (2019)
- [A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning](https://mlsanity.com/paper/2205.13218/) (2022)
- [FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning](https://mlsanity.com/paper/2211.13131/) (2022)
- [A continual learning survey: Defying forgetting in classification tasks](https://mlsanity.com/paper/1909.08383/) (2019)
- [Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning](https://mlsanity.com/paper/2203.06359/) (2022)
- [Deep Feature Interpolation for Image Content Changes](https://mlsanity.com/paper/1611.05507/) (2016)
- [A Theoretical Study on Solving Continual Learning](https://mlsanity.com/paper/2211.02633/) (2022)
- [Regularizing Deep Networks with Semantic Data Augmentation](https://mlsanity.com/paper/2007.10538/) (2020)
- [R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning](https://mlsanity.com/paper/2203.13104/) (2022)
- [Class-Incremental Exemplar Compression for Class-Incremental Learning](https://mlsanity.com/paper/2303.14042/) (2023)
- [Dense Network Expansion for Class Incremental Learning](https://mlsanity.com/paper/2303.12696/) (2023)
- [Continual Semantic Segmentation with Automatic Memory Sample Selection](https://mlsanity.com/paper/2304.05015/) (2023)

## Built on by (most cited first, 6 of 6)

- [CEAT: Continual Expansion and Absorption Transformer for Non-Exemplar Class-Incremental Learning](https://mlsanity.com/paper/2403.06670/) (2024)
- [CSTA: Spatial-Temporal Causal Adaptive Learning for Exemplar-Free Video Class-Incremental Learning](https://mlsanity.com/paper/2501.07236/) (2025)
- [Learn by Reasoning: Analogical Weight Generation for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2503.21258/) (2025)
- [Controllable Relation Disentanglement for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2403.11070/) (2024)
- [Sculpting Margin Penalty: Intra-Task Adapter Merging and Classifier Calibration for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2508.05094/) (2025)
- [DRL: Discriminative Representation Learning with Parallel Adapters for Class Incremental Learning](https://mlsanity.com/paper/2510.12107/) (2025)

## Abstract (arXiv)

Non-exemplar class-incremental learning refers to classifying new and old classes without storing samples of old classes. Since only new class samples are available for optimization, it often occurs catastrophic forgetting of old knowledge. To alleviate this problem, many new methods are proposed such as model distillation, class augmentation. In this paper, we propose an effective non-exemplar method called RAMF consisting of Random Auxiliary classes augmentation and Mixed Feature. On the one hand, we design a novel random auxiliary classes augmentation method, where one augmentation is randomly selected from three augmentations and applied on the input to generate augmented samples and extra class labels. By extending data and label space, it allows the model to learn more diverse representations, which can prevent the model from being biased towards learning task-specific features. When learning new tasks, it will reduce the change of feature space and improve model generalization. On the other hand, we employ mixed feature to replace the new features since only using new feature to optimize the model will affect the representation that was previously embedded in the feature space. Instead, by mixing new and old features, old knowledge can be retained without increasing the computational complexity. Extensive experiments on three benchmarks demonstrate the superiority of our approach, which outperforms the state-of-the-art non-exemplar methods and is comparable to high-performance replay-based methods.