Streaming LifeLong Learning With Any-Time Inference

• URL: http://arxiv.org/abs/2301.11892v1
• Date: Fri, 27 Jan 2023 18:09:19 GMT
• Title: Streaming LifeLong Learning With Any-Time Inference
• Authors: Soumya Banerjee, Vinay Kumar Verma, Vinay P. Namboodiri
• Abstract summary: We propose a novel lifelong learning approach, which is streaming, i.e., a single input sample arrives in each time step, single pass, class-incremental, and subject to be evaluated at any moment. We additionally propose an implicit regularizer in the form of snap-shot self-distillation, which effectively minimizes the forgetting further. Our empirical evaluations and ablations demonstrate that the proposed method outperforms the prior works by large margins.
• Score: 36.3326483579511
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Despite rapid advancements in lifelong learning (LLL) research, a large body of research mainly focuses on improving the performance in the existing \textit{static} continual learning (CL) setups. These methods lack the ability to succeed in a rapidly changing \textit{dynamic} environment, where an AI agent needs to quickly learn new instances in a `single pass' from the non-i.i.d (also possibly temporally contiguous/coherent) data streams without suffering from catastrophic forgetting. For practical applicability, we propose a novel lifelong learning approach, which is streaming, i.e., a single input sample arrives in each time step, single pass, class-incremental, and subject to be evaluated at any moment. To address this challenging setup and various evaluation protocols, we propose a Bayesian framework, that enables fast parameter update, given a single training example, and enables any-time inference. We additionally propose an implicit regularizer in the form of snap-shot self-distillation, which effectively minimizes the forgetting further. We further propose an effective method that efficiently selects a subset of samples for online memory rehearsal and employs a new replay buffer management scheme that significantly boosts the overall performance. Our empirical evaluations and ablations demonstrate that the proposed method outperforms the prior works by large margins.

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