VERSE: Virtual-Gradient Aware Streaming Lifelong Learning with Anytime Inference
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866917592486117376 |
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| author | Banerjee, Soumya Verma, Vinay K. Mukherjee, Avideep Gupta, Deepak Namboodiri, Vinay P. Rai, Piyush |
| author_facet | Banerjee, Soumya Verma, Vinay K. Mukherjee, Avideep Gupta, Deepak Namboodiri, Vinay P. Rai, Piyush |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_08227 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | VERSE: Virtual-Gradient Aware Streaming Lifelong Learning with Anytime Inference Banerjee, Soumya Verma, Vinay K. Mukherjee, Avideep Gupta, Deepak Namboodiri, Vinay P. Rai, Piyush Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | VERSE: Virtual-Gradient Aware Streaming Lifelong Learning with Anytime Inference |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2309.08227 |