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arXiv · 2209.01558

Scalable Adversarial Online Continual Learning

Tanmoy Dam·Mahardhika Pratama·MD Meftahul Ferdaus·Sreenatha Anavatti·Hussein Abbas
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Abstract

Adversarial continual learning is effective for continual learning problems because of the presence of feature alignment process generating task-invariant features having low susceptibility to the catastrophic forgetting problem. Nevertheless, the ACL method imposes considerable complexities because it relies on task-specific networks and discriminators. It also goes through an iterative training process which does not fit for online (one-epoch) continual learning problems. This paper proposes a scalable adversarial continual learning (SCALE) method putting forward a parameter generator transforming common features into task-specific features and a single discriminator in the adversarial game to induce common features. The training process is carried out in meta-learning fashions using a new combination of three loss functions. SCALE outperforms prominent baselines with noticeable margins in both accuracy and execution time.

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Tanmoy Dam, Mahardhika Pratama, MD Meftahul Ferdaus, Sreenatha Anavatti, Hussein Abbas. 2022-09-04. Scalable Adversarial Online Continual Learning. https://arxiv.org/abs/2209.01558

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