Toward Sustainable Continual Learning: Detection and Knowledge Repurposing of Similar Tasks

• URL: http://arxiv.org/abs/2210.05751v1
• Date: Tue, 11 Oct 2022 19:35:30 GMT
• Title: Toward Sustainable Continual Learning: Detection and Knowledge Repurposing of Similar Tasks
• Authors: Sijia Wang, Yoojin Choi, Junya Chen, Mostafa El-Khamy, and Ricardo Henao
• Abstract summary: We introduce a paradigm where the continual learner gets a sequence of mixed similar and dissimilar tasks. We propose a new continual learning framework that uses a task similarity detection function that does not require additional learning. Our experiments show that the proposed framework performs competitively on widely used computer vision benchmarks.
• Score: 31.095642850920385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most existing works on continual learning (CL) focus on overcoming the catastrophic forgetting (CF) problem, with dynamic models and replay methods performing exceptionally well. However, since current works tend to assume exclusivity or dissimilarity among learning tasks, these methods require constantly accumulating task-specific knowledge in memory for each task. This results in the eventual prohibitive expansion of the knowledge repository if we consider learning from a long sequence of tasks. In this work, we introduce a paradigm where the continual learner gets a sequence of mixed similar and dissimilar tasks. We propose a new continual learning framework that uses a task similarity detection function that does not require additional learning, with which we analyze whether there is a specific task in the past that is similar to the current task. We can then reuse previous task knowledge to slow down parameter expansion, ensuring that the CL system expands the knowledge repository sublinearly to the number of learned tasks. Our experiments show that the proposed framework performs competitively on widely used computer vision benchmarks such as CIFAR10, CIFAR100, and EMNIST.

Related papers

• Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning [79.07658065326592]
  Quantifying uncertainty in the estimated tasks is of pivotal importance for many downstream applications, such as online or active learning. We provide novel multitask confidence intervals in the challenging setting when neither the similarity between tasks nor the tasks' features are available to the learner. We propose a novel online learning algorithm that achieves such improved regret without knowing this parameter in advance.
  arXiv  Detail & Related papers  (2023-08-03T13:08:09Z)
• 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)
• Is Multi-Task Learning an Upper Bound for Continual Learning? [26.729088618251282]
  This paper proposes a novel continual self-supervised learning setting, where each task corresponds to learning an invariant representation for a specific class of data augmentations. We show that continual learning often beats multi-task learning on various benchmark datasets, including MNIST, CIFAR-10, and CIFAR-100.
  arXiv  Detail & Related papers  (2022-10-26T15:45:11Z)
• Continual Prompt Tuning for Dialog State Tracking [58.66412648276873]
  A desirable dialog system should be able to continually learn new skills without forgetting old ones. We present Continual Prompt Tuning, a parameter-efficient framework that not only avoids forgetting but also enables knowledge transfer between tasks.
  arXiv  Detail & Related papers  (2022-03-13T13:22:41Z)
• Relational Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship [54.73817402934303]
  We propose Experience Continual Replay (ERR), a bi-level learning framework to adaptively tune task-wise to achieve a better stability plasticity' tradeoff. ERR can consistently improve the performance of all baselines and surpass current state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-31T12:05:22Z)
• 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)
• Efficient Continual Learning with Modular Networks and Task-Driven Priors [31.03712334701338]
  Existing literature in Continual Learning (CL) has focused on overcoming catastrophic forgetting. We introduce a new modular architecture, whose modules represent atomic skills that can be composed to perform a certain task. Our learning algorithm leverages a task-driven prior over the exponential search space of all possible ways to combine modules, enabling efficient learning on long streams of tasks.
  arXiv  Detail & Related papers  (2020-12-23T12:42:16Z)
• Bilevel Continual Learning [76.50127663309604]
  We present a novel framework of continual learning named "Bilevel Continual Learning" (BCL) Our experiments on continual learning benchmarks demonstrate the efficacy of the proposed BCL compared to many state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-30T16:00:23Z)
• 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)
• Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey [53.73359052511171]
  Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. We present a framework for curriculum learning (CL) in RL, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals.
  arXiv  Detail & Related papers  (2020-03-10T20:41:24Z)

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.