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Lifelong learning gets better with MixUp and unsupervised continual representation

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Abstract

Continual learning enables learning systems to adapt to evolving data distributions by sequentially acquiring knowledge from a series of tasks. Unsupervised lifelong learning refers to the ability to learn over time while memorizing previous patterns without supervision. However, the prior methods in this field heavily rely on supervised or reinforcement learning, which necessitates annotated data, thereby limiting their scalability in real-world applications where data is often biased and lacks annotations. To overcome these challenges, this work introduces a novel approach called Lifelong Learning gets better with MixUp and Unsupervised Continual Representation (LL-UCR). LL-UCR aims to learn feature representations from unlabeled tasks, eliminating the need for annotated data. Within the LL-UCR framework, two innovative techniques are introduced: LL-MixUp, which mitigates catastrophic forgetting by interpolating samples between current and previous tasks, and Dark Experience Replay (DER) Buzzega et al. (Adv Neural Inf Process Syst, 33, 15920–15930 2020) adapted for UCR, aligning network logits across tasks. To overcome buffer size limitations in replay-based methods, the Retrospective Adversarial Replay (RAR) framework is incorporated, facilitating diverse replay sample generation. Through systematic analysis, we demonstrate that unsupervised visual representations exhibit remarkable resilience to catastrophic forgetting, consistently outperforming supervised methods in terms of performance and generalization on out-of-distribution tasks. Furthermore, our qualitative analysis reveals that LL-UCR fosters a smoother loss landscape and acquires meaningful feature representations. Extensive experimental evaluations conducted on diverse datasets validate the superior performance of LL-UCR compared to state-of-the-art supervised continual learning methods and the unsupervised LUMP Madaan et al. (International conference on learning representations, 2020) method, effectively mitigating catastrophic forgetting.

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Data availability and access

We used publicly available datasets: split CIFAR-10 [42], split CIFAR-100 [42], and Split Tiny-ImageNet [3] datasets. As these datasets are publicly accessible, no ethical approval or informed consent was required.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Indian Institute of Technology, Roorkee, Roorkee, Uttrakhand, 247667, India

    Prashant kumar & Durga Toshniwal

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  1. Prashant kumar
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  2. Durga Toshniwal
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Contributions

Prashant Kumar: Conceptualization, Methodology, Validation, Writing - original draft. Durga Toshniwal: Writing - review & editing.

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Correspondence to Durga Toshniwal.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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In this paper, the dataset names are mentioned clearly, and it is stated that these datasets are publicly available. Additionally, it is stated that no ethical approval or informed consent was required for the usage of these datasets.

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kumar, P., Toshniwal, D. Lifelong learning gets better with MixUp and unsupervised continual representation. Appl Intell (2024). https://doi.org/10.1007/s10489-024-05434-w

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