VERSE: Virtual-Gradient Aware Streaming Lifelong Learning with Anytime Inference

• URL: http://arxiv.org/abs/2309.08227v2
• Date: Mon, 19 Feb 2024 06:49:50 GMT
• Title: VERSE: Virtual-Gradient Aware Streaming Lifelong Learning with Anytime Inference
• Authors: Soumya Banerjee, Vinay K. Verma, Avideep Mukherjee, Deepak Gupta, Vinay P. Namboodiri, Piyush Rai
• Abstract summary: Streaming lifelong learning is a challenging setting of lifelong learning with the goal of continuous learning without forgetting. We introduce a novel approach to lifelong learning, which is streaming (observes each training example only once) We propose a novel emphvirtual gradients based approach for continual representation learning which adapts to each new example while also generalizing well on past data to prevent catastrophic forgetting.
• Score: 36.61783715563126
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Lifelong learning or continual learning is the problem of training an AI agent continuously while also preventing it from forgetting its previously acquired knowledge. Streaming lifelong learning is a challenging setting of lifelong learning with the goal of continuous learning in a dynamic non-stationary environment without forgetting. We introduce a novel approach to lifelong learning, which is streaming (observes each training example only once), requires a single pass over the data, can learn in a class-incremental manner, and can be evaluated on-the-fly (anytime inference). To accomplish these, we propose a novel \emph{virtual gradients} based approach for continual representation learning which adapts to each new example while also generalizing well on past data to prevent catastrophic forgetting. Our approach also leverages an exponential-moving-average-based semantic memory to further enhance performance. Experiments on diverse datasets with temporally correlated observations demonstrate our method's efficacy and superior performance over existing methods.

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