Selective Hypothesis Transfer for Lifelong Learning

Benavides-Prado, Diana; Koh, Yun Sing; Riddle, Patricia

doi:10.1109/ijcnn.2019.8851778

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Numerous CL algorithms have been developed in the literature (Srivastava et al, 2013;Farquhar & Gal, 2018;Kim et al, 2018;Morgado & Vasconcelos, 2019;Benavides-Prado et al, 2020;Parisi & Lomonaco, 2020;Ahn et al, 2021;Ayub & Wagner, 2021;Cha et al, 2021a;Derakhshani et al, 2021;Ehret et al, 2021;Hurtado et al, 2021;Kapoor et al, 2021;Mao et al, 2021;Tang & Matteson, 2021;Yoon et al, 2021;Benavides-Prado & Riddle, 2022;Madaan et al, 2022;Ramesh & Chaudhari, 2022;Romero et al, 2022a;Skantze & Willemsen, 2022;Wang et al, 2022b;Gaya et al, 2023;Mundt et al, 2023). On a high level, there are three principal approaches to continual learning (CL): memory-based, regularization-based and architecture-based (Pan et al, 2020;Parisi & Lomonaco, 2020;Krishnan & Balaprakash, 2021;Mehta et al, 2021;.…”

Section: Related Workmentioning

confidence: 99%

Similarity-Based Adaptation for Task-Aware and Task-Free Continual Learning

Adel

2024

jair

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Continual learning (CL) is a paradigm which addresses the issue of how to learn from sequentially arriving tasks. The goal of this paper is to introduce a CL framework which can both learn from a global multi-task architecture and locally adapt this learning to the task at hand. In addition to the global knowledge, we conjecture that it is also beneficial to further focus on the most relevant pieces of previous knowledge. Using a prototypical network as a proxy, the proposed framework bases its adaptation on the similarity between the current data stream and the previously encountered data. We develop two algorithms, one for the standard task-aware CL and another for the more challenging task-free setting where boundaries between tasks are unknown. We correspondingly derive a generalization upper bound on the error of an upcoming task. Experiments demonstrate that the introduced algorithms lead to improved performance on several CL benchmarks.

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Section: Related Workmentioning

confidence: 99%

Similarity-Based Adaptation for Task-Aware and Task-Free Continual Learning

Adel

2024

jair

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“…This fundamental ability of lifelong machine learning systems (Silver & Poirier, 2007;Silver et al, 2013) has received very little attention. A first approximation to transferring backward explicitly was presented by us (Benavides-Prado, Koh, & Riddle, 2019). Research such as ELLA (Ruvolo & Eaton, 2013b) has approximated this problem implicitly in the context of multitask learning.…”

Section: Introductionmentioning

confidence: 99%

Towards Knowledgeable Supervised Lifelong Learning Systems

Benavides-Prado

Koh²,

Riddle³

2020

jair

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Learning a sequence of tasks is a long-standing challenge in machine learning. This setting applies to learning systems that observe examples of a range of tasks at different points in time. A learning system should become more knowledgeable as more related tasks are learned. Although the problem of learning sequentially was acknowledged for the first time decades ago, the research in this area has been rather limited. Research in transfer learning, multitask learning, metalearning and deep learning has studied some challenges of these kinds of systems. Recent research in lifelong machine learning and continual learning has revived interest in this problem. We propose Proficiente, a full framework for long-term learning systems. Proficiente relies on knowledge transferred between hypotheses learned with Support Vector Machines. The first component of the framework is focused on transferring forward selectively from a set of existing hypotheses or functions representing knowledge acquired during previous tasks to a new target task. A second component of Proficiente is focused on transferring backward, a novel ability of long-term learning systems that aim to exploit knowledge derived from recent tasks to encourage refinement of existing knowledge. We propose a method that transfers selectively from a task learned recently to existing hypotheses representing previous tasks. The method encourages retention of existing knowledge whilst refining. We analyse the theoretical properties of the proposed framework. Proficiente is accompanied by an agnostic metric that can be used to determine if a long-term learning system is becoming more knowledgeable. We evaluate Proficiente in both synthetic and real-world datasets, and demonstrate scenarios where knowledgeable supervised learning systems can be achieved by means of transfer.

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