CLeaR: An Adaptive Continual Learning Framework for Regression Tasks

• URL: http://arxiv.org/abs/2101.00926v2
• Date: Wed, 17 Feb 2021 16:33:24 GMT
• Title: CLeaR: An Adaptive Continual Learning Framework for Regression Tasks
• Authors: Yujiang He, Bernhard Sick
• Abstract summary: Catastrophic forgetting means that a trained neural network model gradually forgets the previously learned tasks when being retrained on new tasks. Numerous continual learning algorithms are very successful in incremental learning of classification tasks. This article proposes a new methodological framework that can forecast targets and update itself by means of continual learning.
• Score: 2.043835539102076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Catastrophic forgetting means that a trained neural network model gradually forgets the previously learned tasks when being retrained on new tasks. Overcoming the forgetting problem is a major problem in machine learning. Numerous continual learning algorithms are very successful in incremental learning of classification tasks, where new samples with their labels appear frequently. However, there is currently no research that addresses the catastrophic forgetting problem in regression tasks as far as we know. This problem has emerged as one of the primary constraints in some applications, such as renewable energy forecasts. This article clarifies problem-related definitions and proposes a new methodological framework that can forecast targets and update itself by means of continual learning. The framework consists of forecasting neural networks and buffers, which store newly collected data from a non-stationary data stream in an application. The changed probability distribution of the data stream, which the framework has identified, will be learned sequentially. The framework is called CLeaR (Continual Learning for Regression Tasks), where components can be flexibly customized for a specific application scenario. We design two sets of experiments to evaluate the CLeaR framework concerning fitting error (training), prediction error (test), and forgetting ratio. The first one is based on an artificial time series to explore how hyperparameters affect the CLeaR framework. The second one is designed with data collected from European wind farms to evaluate the CLeaR framework's performance in a real-world application. The experimental results demonstrate that the CLeaR framework can continually acquire knowledge in the data stream and improve the prediction accuracy. The article concludes with further research issues arising from requirements to extend the framework.

Related papers

• Adaptive Rentention & Correction for Continual Learning [114.5656325514408]
  A common problem in continual learning is the classification layer's bias towards the most recent task. We name our approach Adaptive Retention & Correction (ARC) ARC achieves an average performance increase of 2.7% and 2.6% on the CIFAR-100 and Imagenet-R datasets.
  arXiv  Detail & Related papers  (2024-05-23T08:43:09Z)
• Fixed Random Classifier Rearrangement for Continual Learning [0.5439020425819]
  In visual classification scenario, neural networks inevitably forget the knowledge of old tasks after learning new ones. We propose a continual learning algorithm named Fixed Random Rearrangement (FRCR)
  arXiv  Detail & Related papers  (2024-02-23T09:43:58Z)
• Enhancing Consistency and Mitigating Bias: A Data Replay Approach for Incremental Learning [100.7407460674153]
  Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks. To mitigate the problem, a line of methods propose to replay the data of experienced tasks when learning new tasks. However, it is not expected in practice considering the memory constraint or data privacy issue. As a replacement, data-free data replay methods are proposed by inverting samples from the classification model.
  arXiv  Detail & Related papers  (2024-01-12T12:51:12Z)
• Complementary Learning Subnetworks for Parameter-Efficient Class-Incremental Learning [40.13416912075668]
  We propose a rehearsal-free CIL approach that learns continually via the synergy between two Complementary Learning Subnetworks. Our method achieves competitive results against state-of-the-art methods, especially in accuracy gain, memory cost, training efficiency, and task-order.
  arXiv  Detail & Related papers  (2023-06-21T01:43:25Z)
• Mitigating Catastrophic Forgetting in Task-Incremental Continual Learning with Adaptive Classification Criterion [50.03041373044267]
  We propose a Supervised Contrastive learning framework with adaptive classification criterion for Continual Learning. Experiments show that CFL achieves state-of-the-art performance and has a stronger ability to overcome compared with the classification baselines.
  arXiv  Detail & Related papers  (2023-05-20T19:22:40Z)
• Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shift [3.303002683812084]
  We propose a scalable and easily adaptable approach based on an informative regularization and an ad-hoc training procedure to mitigate the shift problem. Our method provides a new path to incorporate knowledge from characteristic features into artificial neural networks to manage the underlying data shift problem.
  arXiv  Detail & Related papers  (2023-03-12T02:49:19Z)
• Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment [72.50906475214457]
  The goal of sequential event prediction is to estimate the next event based on a sequence of historical events. In practice, the next-event prediction models are trained with sequential data collected at one time. We propose a framework with hierarchical branching structures for learning context-specific representations.
  arXiv  Detail & Related papers  (2022-10-24T07:54:13Z)
• FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations [55.131858975133085]
  FIRE is a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. We propose ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function. We show that FIRE reduces costs compared to vanilla RL and the greedy baseline in the event of failures.
  arXiv  Detail & Related papers  (2022-09-28T19:49:39Z)
• New Insights on Reducing Abrupt Representation Change in Online Continual Learning [69.05515249097208]
  We focus on the change in representations of observed data that arises when previously unobserved classes appear in the incoming data stream. We show that applying Experience Replay causes the newly added classes' representations to overlap significantly with the previous classes. We propose a new method which mitigates this issue by shielding the learned representations from drastic adaptation to accommodate new classes.
  arXiv  Detail & Related papers  (2022-03-08T01:37:00Z)
• Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts [0.0]
  Two continual learning scenarios will be proposed to describe the potential challenges in this context. 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. 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.
  arXiv  Detail & Related papers  (2021-08-24T14:59:10Z)
• Generative Feature Replay with Orthogonal Weight Modification for Continual Learning [20.8966035274874]
  generative replay is a promising strategy which generates and replays pseudo data for previous tasks to alleviate catastrophic forgetting. We propose to replay penultimate layer feature with a generative model; 2) leverage a self-supervised auxiliary task to further enhance the stability of feature. Empirical results on several datasets show our method always achieves substantial improvement over powerful OWM.
  arXiv  Detail & Related papers  (2020-05-07T13:56:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.