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Qicheng Lao

Alumni

Publications

A Two-Stream Continual Learning System With Variational Domain-Agnostic Feature Replay
Qicheng Lao
Xiang Jiang
Mohammad Havaei
Yoshua Bengio
Learning in nonstationary environments is one of the biggest challenges in machine learning. Nonstationarity can be caused by either task dr… (see more)ift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This article aims to tackle this challenge with a modularized two-stream continual learning (CL) system, where the model is required to learn new tasks from a support stream and adapted to new domains in the query stream while maintaining previously learned knowledge. To deal with both drifts within and across the two streams, we propose a variational domain-agnostic feature replay-based approach that decouples the system into three modules: an inference module that filters the input data from the two streams into domain-agnostic representations, a generative module that facilitates the high-level knowledge transfer, and a solver module that applies the filtered and transferable knowledge to solve the queries. We demonstrate the effectiveness of our proposed approach in addressing the two fundamental scenarios and complex scenarios in two-stream CL.
2021-03-03
IEEE Transactions on Neural Networks and Learning Systems (published)
doi.org
Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay
Qicheng Lao
Xiang Jiang
Mohammad Havaei
Yoshua Bengio
Learning in non-stationary environments is one of the biggest challenges in machine learning. Non-stationarity can be caused by either task … (see more)drift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This paper aims to tackle this challenge in the context of continuous domain adaptation, where the model is required to learn new tasks adapted to new domains in a non-stationary environment while maintaining previously learned knowledge. To deal with both drifts, we propose variational domain-agnostic feature replay, an approach that is composed of three components: an inference module that filters the input data into domain-agnostic representations, a generative module that facilitates knowledge transfer, and a solver module that applies the filtered and transferable knowledge to solve the queries. We address the two fundamental scenarios in continuous domain adaptation, demonstrating the effectiveness of our proposed approach for practical usage.
2020-03-09
ArXiv (preprint)
arxiv.org
arxiv.org