# Overcoming Catastrophic Forgetting in Tabular Data Classification: A Pseudorehearsal-based approach

arXiv: https://arxiv.org/abs/2407.09039 · HTML: https://mlsanity.com/paper/2407.09039/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 64 references that are in the map.
- First posted on arXiv 12 Jul 2024.

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

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [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)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Class-incremental learning: survey and performance evaluation on image classification](https://mlsanity.com/paper/2010.15277/) (2020)
- [On Tiny Episodic Memories in Continual Learning](https://mlsanity.com/paper/1902.10486/) (2019)
- [Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting](https://mlsanity.com/paper/1904.00310/) (2019)
- [Online Continual Learning in Image Classification: An Empirical Survey](https://mlsanity.com/paper/2101.10423/) (2021)
- [FearNet: Brain-Inspired Model for Incremental Learning](https://mlsanity.com/paper/1711.10563/) (2017)
- [Continual Lifelong Learning in Natural Language Processing: A Survey](https://mlsanity.com/paper/2012.09823/) (2020)
- [Generative replay with feedback connections as a general strategy for continual learning](https://mlsanity.com/paper/1809.10635/) (2018)
- [Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges](https://mlsanity.com/paper/1907.00182/) (2019)
- [Rehearsal revealed: The limits and merits of revisiting samples in continual learning](https://mlsanity.com/paper/2104.07446/) (2021)
- [Gradient-based Editing of Memory Examples for Online Task-free Continual Learning](https://mlsanity.com/paper/2006.15294/) (2020)
- [Deep Generative Dual Memory Network for Continual Learning](https://mlsanity.com/paper/1710.10368/) (2017)
- [Recent Advances of Continual Learning in Computer Vision: An Overview](https://mlsanity.com/paper/2109.11369/) (2021)
- [Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic Forgetting](https://mlsanity.com/paper/1812.02464/) (2018)
- [Avalanche: A PyTorch Library for Deep Continual Learning](https://mlsanity.com/paper/2302.01766/) (2023)
- [An Investigation of Replay-based Approaches for Continual Learning](https://mlsanity.com/paper/2108.06758/) (2021)
- [Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep Neural Networks](https://mlsanity.com/paper/1802.03875/) (2018)
- [Task-Free Continual Learning via Online Discrepancy Distance Learning](https://mlsanity.com/paper/2210.06579/) (2022)
- [Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing](https://mlsanity.com/paper/2101.00509/) (2021)
- [Overcoming Catastrophic Forgetting by Generative Regularization](https://mlsanity.com/paper/1912.01238/) (2019)
- [Continual Learning with Dirichlet Generative-based Rehearsal](https://mlsanity.com/paper/2309.06917/) (2023)
- [Continual learning of longitudinal health records](https://mlsanity.com/paper/2112.11944/) (2021)
- [Decentralised and collaborative machine learning framework for IoT](https://mlsanity.com/paper/2312.12190/) (2023)
- [Continual Learning with Invertible Generative Models](https://mlsanity.com/paper/2202.05694/) (2022)

## Built on by (most cited first, 5 of 5)

- [Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains](https://mlsanity.com/paper/2604.17089/) (2026)
- [Continual Learning at the Edge: An Agnostic IIoT Architecture](https://mlsanity.com/paper/2512.14311/) (2025)
- [Continual Learning for non-stationary regression via Memory-Efficient Replay](https://mlsanity.com/paper/2602.09720/) (2026)
- [MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound](https://mlsanity.com/paper/2605.11617/) (2026)
- [Combining Statistical Features and Deep Encodings for Rehearsal-Based Class-Incremental Time Series Classification](https://mlsanity.com/paper/2606.03292/) (2026)

## Abstract (arXiv)

Continual learning (CL) poses the important challenge of adapting to evolving data distributions without forgetting previously acquired knowledge while consolidating new knowledge. In this paper, we introduce a new methodology, coined as Tabular-data Rehearsal-based Incremental Lifelong Learning framework (TRIL3), designed to address the phenomenon of catastrophic forgetting in tabular data classification problems. TRIL3 uses the prototype-based incremental generative model XuILVQ to generate synthetic data to preserve old knowledge and the DNDF algorithm, which was modified to run in an incremental way, to learn classification tasks for tabular data, without storing old samples. After different tests to obtain the adequate percentage of synthetic data and to compare TRIL3 with other CL available proposals, we can conclude that the performance of TRIL3 outstands other options in the literature using only 50% of synthetic data.