Lifelong Learning Without a Task Oracle
Amanda Rios, Laurent Itti

TL;DR
This paper introduces lightweight, task-agnostic methods for continual learning that effectively mitigate catastrophic forgetting without relying on explicit task labels, achieving near-oracle performance with minimal memory overhead.
Contribution
The paper proposes simple, memory-efficient task-assigning models for continual learning that perform comparably to task-oracle methods, without requiring task labels.
Findings
Methods perform close to a task oracle in inter-dataset scenarios.
Achieve only 1.7% increase in parameter memory on average.
Effective in both multi-dataset sequences and within-single-dataset tasks.
Abstract
Supervised deep neural networks are known to undergo a sharp decline in the accuracy of older tasks when new tasks are learned, termed "catastrophic forgetting". Many state-of-the-art solutions to continual learning rely on biasing and/or partitioning a model to accommodate successive tasks incrementally. However, these methods largely depend on the availability of a task-oracle to confer task identities to each test sample, without which the models are entirely unable to perform. To address this shortcoming, we propose and compare several candidate task-assigning mappers which require very little memory overhead: (1) Incremental unsupervised prototype assignment using either nearest means, Gaussian Mixture Models or fuzzy ART backbones; (2) Supervised incremental prototype assignment with fast fuzzy ARTMAP; (3) Shallow perceptron trained via a dynamic coreset. Our proposed model…
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Taxonomy
TopicsDomain Adaptation and Few-Shot Learning · Multimodal Machine Learning Applications · Human Pose and Action Recognition
