Selective Replay Enhances Learning in Online Continual Analogical Reasoning
By Tyler L. Hayes, Christopher Kanan. 2021
26 citations recorded by the source corpus.
In continual learning, a system learns from non-stationary data streams or batches without catastrophic forgetting. While this problem has been heavily studied in supervised image classification and reinforcement learning, continual learning in neural networks designed for abstract reasoning has not yet been studied. Here, we study continual learning of analogical reasoning. Analogical reasoning tests such as Raven's Progressive Matrices (RPMs) are commonly used to measure non-verbal abstract reasoning in humans, and recently offline neural networks for the RPM problem have been proposed. In this paper, we establish experimental baselines, protocols, and forward and backward transfer metrics to evaluate continual learners on RPMs. We employ experience replay to mitigate catastrophic forgetting. Prior work using replay for image classification tasks has found that selectively choosing the samples to replay offers little, if any, benefit over random selection. In contrast, we find that selective replay can significantly outperform random selection for the RPM task1.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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In Artificial Intelligence
In Computer Vision and Pattern Recognition
- René Vidal
- Paris V. Giampouras
- Liangzu Peng
- Ehsan Elhamifar
- Paolo Favaro
- Shuicheng Yan
- Zhouchen Lin
- Jitendra Malik
Citations in the corpus
- The Ideal Continual Learner: An Agent That Never Forgets (2023, background): “More recently, based on Isele & Cosgun (2018); Hayes & Kanan (2021), Araujo et al. (2022) further included three more basic methods, and compared the seven methods for natural language processing applications.”
- Recent Advances of Continual Learning in Computer Vision: An Overview (2021, neutral): “Hayes and Kanan [101] proposed an analogical reasoning method, where Raven’s Progressive Matrices [102] are commonly used as the measurement method.”
- Experts Collaboration Learning for Continual Multi-Modal Reasoning (2023, unknown): “tion [2], [7], [8], [9], [10], [11], [12].”