# AdaptCL: Adaptive Continual Learning for Tackling Heterogeneity in Sequential Datasets

arXiv: https://arxiv.org/abs/2207.11005 · HTML: https://mlsanity.com/paper/2207.11005/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 52 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Builds on (most cited first, 28 of 28)

- [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)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [Memory Aware Synapses: Learning what (not) to forget](https://mlsanity.com/paper/1711.09601/) (2017)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Overcoming catastrophic forgetting with hard attention to the task](https://mlsanity.com/paper/1801.01423/) (2018)
- [Progress & Compress: A scalable framework for continual learning](https://mlsanity.com/paper/1805.06370/) (2018)
- [PathNet: Evolution Channels Gradient Descent in Super Neural Networks](https://mlsanity.com/paper/1701.08734/) (2017)
- [Reinforced Continual Learning](https://mlsanity.com/paper/1805.12369/) (2018)
- [Conditional Channel Gated Networks for Task-Aware Continual Learning](https://mlsanity.com/paper/2004.00070/) (2020)
- [Incremental Learning Through Deep Adaptation](https://mlsanity.com/paper/1705.04228/) (2017)
- [Continual Learning via Neural Pruning](https://mlsanity.com/paper/1903.04476/) (2019)
- [Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks](https://mlsanity.com/paper/2112.10017/) (2021)
- [Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers](https://mlsanity.com/paper/2005.06870/) (2020)
- [Memory Efficient Class-Incremental Learning for Image Classification](https://mlsanity.com/paper/2008.01411/) (2020)
- [Online Continual Learning For Visual Food Classification](https://mlsanity.com/paper/2108.06781/) (2021)
- [Continual Learning with Adaptive Weights (CLAW)](https://mlsanity.com/paper/1911.09514/) (2019)
- [Distilled Replay: Overcoming Forgetting through Synthetic Samples](https://mlsanity.com/paper/2103.15851/) (2021)
- [Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay](https://mlsanity.com/paper/2201.03019/) (2022)
- [Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations](https://mlsanity.com/paper/1810.07411/) (2018)
- [Accurate and Compact Convolutional Neural Networks with Trained Binarization](https://mlsanity.com/paper/1909.11366/) (2019)
- [Continual Learning Using Bayesian Neural Networks](https://mlsanity.com/paper/1910.04112/) (2019)
- [Prototype-Guided Memory Replay for Continual Learning](https://mlsanity.com/paper/2108.12641/) (2021)
- [Exemplar-free Online Continual Learning](https://mlsanity.com/paper/2202.05491/) (2022)

## Built on by (most cited first, 4 of 4)

- [A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends](https://mlsanity.com/paper/2402.15490/) (2024)
- [Federated Continual Learning: Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2502.07059/) (2025)
- [Never-Ending Behavior-Cloning Agent for Robotic Manipulation](https://mlsanity.com/paper/2403.00336/) (2024)
- [HPCR: Holistic Proxy-based Contrastive Replay for Online Continual Learning](https://mlsanity.com/paper/2309.15038/) (2023)

## Abstract (arXiv)

Managing heterogeneous datasets that vary in complexity, size, and similarity in continual learning presents a significant challenge. Task-agnostic continual learning is necessary to address this challenge, as datasets with varying similarity pose difficulties in distinguishing task boundaries. Conventional task-agnostic continual learning practices typically rely on rehearsal or regularization techniques. However, rehearsal methods may struggle with varying dataset sizes and regulating the importance of old and new data due to rigid buffer sizes. Meanwhile, regularization methods apply generic constraints to promote generalization but can hinder performance when dealing with dissimilar datasets lacking shared features, necessitating a more adaptive approach. In this paper, we propose AdaptCL, a novel adaptive continual learning method to tackle heterogeneity in sequential datasets. AdaptCL employs fine-grained data-driven pruning to adapt to variations in data complexity and dataset size. It also utilizes task-agnostic parameter isolation to mitigate the impact of varying degrees of catastrophic forgetting caused by differences in data similarity. Through a two-pronged case study approach, we evaluate AdaptCL on both datasets of MNIST Variants and DomainNet, as well as datasets from different domains. The latter include both large-scale, diverse binary-class datasets and few-shot, multi-class datasets. Across all these scenarios, AdaptCL consistently exhibits robust performance, demonstrating its flexibility and general applicability in handling heterogeneous datasets.