Task-Aware Multi-Expert Architecture For Lifelong Deep Learning

• URL: http://arxiv.org/abs/2512.11243v1
• Date: Fri, 12 Dec 2025 03:05:26 GMT
• Title: Task-Aware Multi-Expert Architecture For Lifelong Deep Learning
• Authors: Jianyu Wang, Jacob Nean-Hua Sheikh, Cat P. Le, Hoda Bidkhori,
• Abstract summary: We propose Task-Aware Multi-Expert (TAME) to guide expert selection and knowledge transfer.<n>A shared dense layer integrates features from the chosen expert to generate predictions.<n>Experiments on binary classification tasks derived from CIFAR-100 show that TAME improves accuracy on new tasks while sustaining performance on earlier ones.
• Score: 5.806256557012355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Lifelong deep learning (LDL) trains neural networks to learn sequentially across tasks while preserving prior knowledge. We propose Task-Aware Multi-Expert (TAME), a continual learning algorithm that leverages task similarity to guide expert selection and knowledge transfer. TAME maintains a pool of pretrained neural networks and activates the most relevant expert for each new task. A shared dense layer integrates features from the chosen expert to generate predictions. To reduce catastrophic forgetting, TAME uses a replay buffer that stores representative samples and embeddings from previous tasks and reuses them during training. An attention mechanism further prioritizes the most relevant stored information for each prediction. Together, these components allow TAME to adapt flexibly while retaining important knowledge across evolving task sequences. Experiments on binary classification tasks derived from CIFAR-100 show that TAME improves accuracy on new tasks while sustaining performance on earlier ones, highlighting its effectiveness in balancing adaptation and retention in lifelong learning settings.

Related papers

• Hierarchical Subspaces of Policies for Continual Offline Reinforcement Learning [19.463863037999054]
  We consider a Continual Reinforcement Learning setup, where a learning agent must continuously adapt to new tasks while retaining previously acquired skill sets.<n>We introduce HiSPO, a novel hierarchical framework designed specifically for continual learning in navigation settings from offline data.<n>We demonstrate, through a careful experimental study, the effectiveness of our method in both classical MuJoCo maze environments and complex video game-like navigation simulations.
  arXiv  Detail & Related papers  (2024-12-19T14:00:03Z)
• 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)
• Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding [52.723297744257536]
  Pre-trained language models (LMs) have shown effectiveness in scientific literature understanding tasks. We propose a multi-task contrastive learning framework, SciMult, to facilitate common knowledge sharing across different literature understanding tasks.
  arXiv  Detail & Related papers  (2023-05-23T16:47:22Z)
• Task-Attentive Transformer Architecture for Continual Learning of Vision-and-Language Tasks Using Knowledge Distillation [18.345183818638475]
  Continual learning (CL) can serve as a remedy through enabling knowledge-transfer across sequentially arriving tasks. We develop a transformer-based CL architecture for learning bimodal vision-and-language tasks. Our approach is scalable learning to a large number of tasks because it requires little memory and time overhead.
  arXiv  Detail & Related papers  (2023-03-25T10:16:53Z)
• Online Continual Learning via the Knowledge Invariant and Spread-out Properties [4.109784267309124]
  Key challenge in continual learning is catastrophic forgetting. We propose a new method, named Online Continual Learning via the Knowledge Invariant and Spread-out Properties (OCLKISP) We empirically evaluate our proposed method on four popular benchmarks for continual learning: Split CIFAR 100, Split SVHN, Split CUB200 and Split Tiny-Image-Net.
  arXiv  Detail & Related papers  (2023-02-02T04:03:38Z)
• Learning Options via Compression [62.55893046218824]
  We propose a new objective that combines the maximum likelihood objective with a penalty on the description length of the skills. Our objective learns skills that solve downstream tasks in fewer samples compared to skills learned from only maximizing likelihood.
  arXiv  Detail & Related papers  (2022-12-08T22:34:59Z)
• Task Residual for Tuning Vision-Language Models [69.22958802711017]
  We propose a new efficient tuning approach for vision-language models (VLMs) named Task Residual Tuning (TaskRes) TaskRes explicitly decouples the prior knowledge of the pre-trained models and new knowledge regarding a target task. The proposed TaskRes is simple yet effective, which significantly outperforms previous methods on 11 benchmark datasets.
  arXiv  Detail & Related papers  (2022-11-18T15:09:03Z)
• Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning [54.7584721943286]
  Continual Learning (CL) methods aim to enable machine learning models to learn new tasks without catastrophic forgetting of those that have been previously mastered. Existing CL approaches often keep a buffer of previously-seen samples, perform knowledge distillation, or use regularization techniques towards this goal. We propose to only activate and select sparse neurons for learning current and past tasks at any stage.
  arXiv  Detail & Related papers  (2022-02-21T13:25:03Z)
• Self-Attention Meta-Learner for Continual Learning [5.979373021392084]
  Self-Attention Meta-Learner (SAM) learns a prior knowledge for continual learning that permits learning a sequence of tasks. SAM incorporates an attention mechanism that learns to select the particular relevant representation for each future task. We evaluate the proposed method on the Split CIFAR-10/100 and Split MNIST benchmarks in the task inference.
  arXiv  Detail & Related papers  (2021-01-28T17:35:04Z)
• Continual Learning in Low-rank Orthogonal Subspaces [86.36417214618575]
  In continual learning (CL), a learner is faced with a sequence of tasks, arriving one after the other, and the goal is to remember all the tasks once the learning experience is finished. The prior art in CL uses episodic memory, parameter regularization or network structures to reduce interference among tasks, but in the end, all the approaches learn different tasks in a joint vector space. We propose to learn tasks in different (low-rank) vector subspaces that are kept orthogonal to each other in order to minimize interference.
  arXiv  Detail & Related papers  (2020-10-22T12:07:43Z)
• Adversarial Continual Learning [99.56738010842301]
  We propose a hybrid continual learning framework that learns a disjoint representation for task-invariant and task-specific features. Our model combines architecture growth to prevent forgetting of task-specific skills and an experience replay approach to preserve shared skills.
  arXiv  Detail & Related papers  (2020-03-21T02:08:17Z)

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.