# Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts

arXiv: https://arxiv.org/abs/2108.10781 · HTML: https://mlsanity.com/paper/2108.10781/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 44 references that are in the map.
- First posted on arXiv 24 Aug 2021.

## Builds on (most cited first, 23 of 23)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [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)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://mlsanity.com/paper/1711.05769/) (2017)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [PathNet: Evolution Channels Gradient Descent in Super Neural Networks](https://mlsanity.com/paper/1701.08734/) (2017)
- [Variational Continual Learning](https://mlsanity.com/paper/1710.10628/) (2017)
- [CORe50: a New Dataset and Benchmark for Continuous Object Recognition](https://mlsanity.com/paper/1705.03550/) (2017)
- [Class-incremental Learning via Deep Model Consolidation](https://mlsanity.com/paper/1903.07864/) (2019)
- [Towards Robust Evaluations of Continual Learning](https://mlsanity.com/paper/1805.09733/) (2018)
- [Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams](https://mlsanity.com/paper/2009.00919/) (2020)
- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)
- [Don't forget, there is more than forgetting: new metrics for Continual Learning](https://mlsanity.com/paper/1810.13166/) (2018)
- [Generative replay with feedback connections as a general strategy for continual learning](https://mlsanity.com/paper/1809.10635/) (2018)
- [Encoder Based Lifelong Learning](https://mlsanity.com/paper/1704.01920/) (2017)
- [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017)
- [Continual Classification Learning Using Generative Models](https://mlsanity.com/paper/1810.10612/) (2018)
- [CLeaR: An Adaptive Continual Learning Framework for Regression Tasks](https://mlsanity.com/paper/2101.00926/) (2021)
- [EVE: Explainable Vector Based Embedding Technique Using Wikipedia](https://mlsanity.com/paper/1702.06891/) (2017)
- [Organic Computing in the Spotlight](https://mlsanity.com/paper/1701.08125/) (2017)

## Built on by (most cited first, 2 of 2)

- [ODEStream: A Buffer-Free Online Learning Framework with ODE-based Adaptor for Streaming Time Series Forecasting](https://mlsanity.com/paper/2411.07413/) (2024)
- [Continual Learning for non-stationary regression via Memory-Efficient Replay](https://mlsanity.com/paper/2602.09720/) (2026)

## Abstract (arXiv)

Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn new tasks and overcome forgetting the knowledge obtained from the old tasks as the amount of data keeps increasing in applications. In this article, two continual learning scenarios will be proposed to describe the potential challenges in this context. Besides, based on our previous work regarding the CLeaR framework, which is short for continual learning for regression tasks, the work will be further developed to enable models to extend themselves and learn data successively. Research topics are related but not limited to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc. Moreover, the framework- and algorithm-related hyperparameters should be dynamically updated in applications. Forecasting experiments will be conducted based on power generation and consumption data collected from real-world applications. A series of comprehensive evaluation metrics and visualization tools can help analyze the experimental results. The proposed framework is expected to be generally applied to other constantly changing scenarios.