AdaHAT: Adaptive Hard Attention to the Task in Task-Incremental Learning

• URL: http://arxiv.org/abs/2608.01252v1
• Date: Sun, 02 Aug 2026 14:03:57 GMT
• Title: AdaHAT: Adaptive Hard Attention to the Task in Task-Incremental Learning
• Abstract summary: Catastrophic forgetting is a major problem in task-incremental learning, where neural networks tend to overwrite previously learned knowledge when trained on new tasks.<n>We propose Adaptive Hard Attention to the Task (AdaHAT) with an adaptive attention mechanism which allows adaptive updates to static parameters.
• Score: 3.655781945138786
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Catastrophic forgetting is a major problem in task-incremental learning, where neural networks tend to overwrite previously learned knowledge when trained on new tasks. A number of architecture-based approaches have been proposed to address this problem. However, the architecture-based approaches suffer from another problem related to network capacity when the networks learn long task sequences: As a network is trained on an increasing number of new tasks in a long task sequence, a growing proportion of active parameters becomes static to prevent forgetting of previously learned knowledge. In this paper, we propose Adaptive Hard Attention to the Task (AdaHAT) with an adaptive attention mechanism which allows adaptive updates to static parameters by taking into account the information about previous tasks on both the importance of these parameters to previous tasks and the current network capacity. Based on this idea, we develop a new neural network architecture incorporating our proposed AdaHAT mechanism. AdaHAT extends an existing architecture-based approach, Hard Attention to the Task (HAT), to better support task-incremental learning over long task sequences. We conduct experiments on a number of datasets and compare AdaHAT with task-incremental learning baselines including HAT. Our experimental results show that AdaHAT achieves better average performance across tasks than these baselines, especially on long task sequences, demonstrating the benefits from balancing the trade-off between stability and plasticity of a network when learning such sequences of tasks, alleviating the network capacity problem. Our code is available at pengxiang-wang.com/projects/continual-learning-arena.

Related papers

• Continual Learning for Adaptive AI Systems [0.0]
  Cluster-Aware Replay (CAR) is a hybrid continual learning framework that integrates a small, class-balanced replay buffer with a regularization term.<n>CAR better preserves earlier task performance compared to fine-tuning alone.
  arXiv  Detail & Related papers  (2025-10-09T00:44:32Z)
• Learning without Isolation: Pathway Protection for Continual Learning [64.3476595369537]
  Deep networks are prone to catastrophic forgetting during sequential task learning.<n>We propose a novel CL framework, learning without isolation(LwI), where model fusion is formulated as graph matching.<n>Thanks to the sparsity of activation channels in a deep network, LwI can adaptively allocate available pathways for a new task, realizing pathway protection and addressing catastrophic forgetting.
  arXiv  Detail & Related papers  (2025-05-24T07:16:55Z)
• Exploiting Task Relationships for Continual Learning Using Transferability-Aware Task Embeddings [8.814732457885022]
  Continual learning (CL) has been a critical topic in contemporary deep neural network applications.<n>We propose a transferability-aware task embedding, termed H-embedding, and construct a hypernet framework under its guidance.
  arXiv  Detail & Related papers  (2025-02-17T09:52:19Z)
• Negotiated Representations to Prevent Forgetting in Machine Learning Applications [0.0]
  Catastrophic forgetting is a significant challenge in the field of machine learning. We propose a novel method for preventing catastrophic forgetting in machine learning applications.
  arXiv  Detail & Related papers  (2023-11-30T22:43:50Z)
• Dense Network Expansion for Class Incremental Learning [61.00081795200547]
  State-of-the-art approaches use a dynamic architecture based on network expansion (NE), in which a task expert is added per task. A new NE method, dense network expansion (DNE), is proposed to achieve a better trade-off between accuracy and model complexity. It outperforms the previous SOTA methods by a margin of 4% in terms of accuracy, with similar or even smaller model scale.
  arXiv  Detail & Related papers  (2023-03-22T16:42:26Z)
• Neural Weight Search for Scalable Task Incremental Learning [6.413209417643468]
  Task incremental learning aims to enable a system to maintain its performance on previously learned tasks while learning new tasks, solving the problem of catastrophic forgetting. One promising approach is to build an individual network or sub-network for future tasks. This leads to an ever-growing memory due to saving extra weights for new tasks and how to address this issue has remained an open problem in task incremental learning.
  arXiv  Detail & Related papers  (2022-11-24T23:30:23Z)
• Continual Learning with Dependency Preserving Hypernetworks [14.102057320661427]
  An effective approach to address continual learning (CL) problems is to use hypernetworks which generate task dependent weights for a target network. We propose a novel approach that uses a dependency preserving hypernetwork to generate weights for the target network while also maintaining the parameter efficiency. In addition, we propose novel regularisation and network growth techniques for the RNN based hypernetwork to further improve the continual learning performance.
  arXiv  Detail & Related papers  (2022-09-16T04:42:21Z)
• E2-AEN: End-to-End Incremental Learning with Adaptively Expandable Network [57.87240860624937]
  We propose an end-to-end trainable adaptively expandable network named E2-AEN. It dynamically generates lightweight structures for new tasks without any accuracy drop in previous tasks. E2-AEN reduces cost and can be built upon any feed-forward architectures in an end-to-end manner.
  arXiv  Detail & Related papers  (2022-07-14T09:04:51Z)
• Multi-Task Learning with Sequence-Conditioned Transporter Networks [67.57293592529517]
  We aim to solve multi-task learning through the lens of sequence-conditioning and weighted sampling. We propose a new suite of benchmark aimed at compositional tasks, MultiRavens, which allows defining custom task combinations. Second, we propose a vision-based end-to-end system architecture, Sequence-Conditioned Transporter Networks, which augments Goal-Conditioned Transporter Networks with sequence-conditioning and weighted sampling.
  arXiv  Detail & Related papers  (2021-09-15T21:19:11Z)
• Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference [75.95287293847697]
  Two common challenges in developing multi-task models are often overlooked in literature. First, enabling the model to be inherently incremental, continuously incorporating information from new tasks without forgetting the previously learned ones (incremental learning) Second, eliminating adverse interactions amongst tasks, which has been shown to significantly degrade the single-task performance in a multi-task setup (task interference)
  arXiv  Detail & Related papers  (2020-07-24T14:44:46Z)
• Auxiliary Learning by Implicit Differentiation [54.92146615836611]
  Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest. Here, we propose a novel framework, AuxiLearn, that targets both challenges based on implicit differentiation. First, when useful auxiliaries are known, we propose learning a network that combines all losses into a single coherent objective function. Second, when no useful auxiliary task is known, we describe how to learn a network that generates a meaningful, novel auxiliary task.
  arXiv  Detail & Related papers  (2020-06-22T19:35:07Z)

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.