# Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning

arXiv: https://arxiv.org/abs/2405.19074 · HTML: https://mlsanity.com/paper/2405.19074/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 60 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [Adversarial Examples Are Not Bugs, They Are Features](https://mlsanity.com/paper/1905.02175/) (2019)
- [Learning to Prompt for Continual Learning](https://mlsanity.com/paper/2112.08654/) (2021)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning](https://mlsanity.com/paper/2204.04799/) (2022)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning](https://mlsanity.com/paper/2211.13218/) (2022)
- [Class-incremental learning: survey and performance evaluation on image classification](https://mlsanity.com/paper/2010.15277/) (2020)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion](https://mlsanity.com/paper/1912.08795/) (2019)
- [Measuring Catastrophic Forgetting in Neural Networks](https://mlsanity.com/paper/1708.02072/) (2017)
- [Learning without Memorizing](https://mlsanity.com/paper/1811.08051/) (2018)
- [Semantic Drift Compensation for Class-Incremental Learning](https://mlsanity.com/paper/2004.00440/) (2020)
- [FOSTER: Feature Boosting and Compression for Class-Incremental Learning](https://mlsanity.com/paper/2204.04662/) (2022)
- [Adversarial Continual Learning](https://mlsanity.com/paper/2003.09553/) (2020)
- [FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning](https://mlsanity.com/paper/2211.13131/) (2022)
- [Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality](https://mlsanity.com/paper/2310.07234/) (2023)
- [Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning](https://mlsanity.com/paper/2203.06359/) (2022)

## Built on by (most cited first, 30 of 33)

- [Exemplar-free Continual Representation Learning via Learnable Drift Compensation](https://mlsanity.com/paper/2407.08536/) (2024)
- [LoRA Subtraction for Drift-Resistant Space in Exemplar-Free Continual Learning](https://mlsanity.com/paper/2503.18985/) (2025)
- [Task-Agnostic Guided Feature Expansion for Class-Incremental Learning](https://mlsanity.com/paper/2503.00823/) (2025)
- [Rethinking the Stability-Plasticity Trade-off in Continual Learning from an Architectural Perspective](https://mlsanity.com/paper/2506.03951/) (2025)
- [What Makes a Good Dataset for Knowledge Distillation?](https://mlsanity.com/paper/2411.12817/) (2024)
- [On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning](https://mlsanity.com/paper/2501.15454/) (2025)
- [Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning](https://mlsanity.com/paper/2502.07560/) (2025)
- [Semantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental Learning](https://mlsanity.com/paper/2503.05423/) (2025)
- [VA-MoE: Variables-Adaptive Mixture of Experts for Incremental Weather Forecasting](https://mlsanity.com/paper/2412.02503/) (2024)
- [How green is continual learning, really? Analyzing the energy consumption in continual training of vision foundation models](https://mlsanity.com/paper/2409.18664/) (2024)
- [Query Drift Compensation: Enabling Compatibility in Continual Learning of Retrieval Embedding Models](https://mlsanity.com/paper/2506.00037/) (2025)
- [AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting](https://mlsanity.com/paper/2505.11817/) (2025)
- [Parameter Efficient Continual Learning with Dynamic Low-Rank Adaptation](https://mlsanity.com/paper/2505.11998/) (2025)
- [Towards Efficient and Exact Forgetting Services in Pre-Trained-Model-based Continual Learning](https://mlsanity.com/paper/2505.12239/) (2025)
- [Elastic Weight Consolidation Done Right for Continual Learning](https://mlsanity.com/paper/2603.18596/) (2026)
- [EFC++: Elastic Feature Consolidation with Prototype Re-balancing for Cold Start Exemplar-free Incremental Learning](https://mlsanity.com/paper/2503.10439/) (2025)
- [Replay to Remember (R2R): An Efficient Uncertainty-driven Unsupervised Continual Learning Framework Using Generative Replay](https://mlsanity.com/paper/2505.04787/) (2025)
- [InTAct: Interval-based Task Activation Consolidation for Continual Learning](https://mlsanity.com/paper/2511.17439/) (2025)
- [Unifying Locality of KANs and Feature Drift Compensation Projection for Data-free Replay based Continual Face Forgery Detection](https://mlsanity.com/paper/2508.03189/) (2025)
- [Continual Learning with Vision-Language Models via Semantic-Geometry Preservation](https://mlsanity.com/paper/2603.12055/) (2026)
- [Continual Distillation of Teachers from Different Domains](https://mlsanity.com/paper/2605.04059/) (2026)
- [Two-Way Is Better Than One: Bidirectional Alignment with Cycle Consistency for Exemplar-Free Class-Incremental Learning](https://mlsanity.com/paper/2606.05675/) (2026)
- [Balanced Gradient Sample Retrieval for Enhanced Knowledge Retention in Proxy-based Continual Learning](https://mlsanity.com/paper/2412.14430/) (2024)
- [WECAR: An End-Edge Collaborative Inference and Training Framework for WiFi-Based Continuous Human Activity Recognition](https://mlsanity.com/paper/2503.07669/) (2025)
- [Restoring Forgotten Knowledge in Non-Exemplar Class Incremental Learning through Test-Time Semantic Evolution](https://mlsanity.com/paper/2503.16793/) (2025)
- [Prototype Augmented Hypernetworks for Continual Learning](https://mlsanity.com/paper/2505.07450/) (2025)
- [Continual Learning Beyond Experience Rehearsal and Full Model Surrogates](https://mlsanity.com/paper/2505.21942/) (2025)
- [Adaptive Model Ensemble for Continual Learning](https://mlsanity.com/paper/2509.19819/) (2025)
- [Adversarial Pseudo-replay for Exemplar-free Class-incremental Learning](https://mlsanity.com/paper/2511.17973/) (2025)
- [Memory-efficient Continual Learning with Prototypical Exemplar Condensation](https://mlsanity.com/paper/2603.13804/) (2026)

## Abstract (arXiv)

Continual learning methods are known to suffer from catastrophic forgetting, a phenomenon that is particularly hard to counter for methods that do not store exemplars of previous tasks. Therefore, to reduce potential drift in the feature extractor, existing exemplar-free methods are typically evaluated in settings where the first task is significantly larger than subsequent tasks. Their performance drops drastically in more challenging settings starting with a smaller first task. To address this problem of feature drift estimation for exemplar-free methods, we propose to adversarially perturb the current samples such that their embeddings are close to the old class prototypes in the old model embedding space. We then estimate the drift in the embedding space from the old to the new model using the perturbed images and compensate the prototypes accordingly. We exploit the fact that adversarial samples are transferable from the old to the new feature space in a continual learning setting. The generation of these images is simple and computationally cheap. We demonstrate in our experiments that the proposed approach better tracks the movement of prototypes in embedding space and outperforms existing methods on several standard continual learning benchmarks as well as on fine-grained datasets. Code is available at https://github.com/dipamgoswami/ADC.