Continual Learning Using Bayesian Neural Networks

By Honglin Li, Payam M. Barnaghi, Shirin Enshaeifar, Frieder Ganz. 2019

46 citations recorded by the source corpus.

Continual learning models allow them to learn and adapt to new changes and tasks over time. However, in continual and sequential learning scenarios, in which the models are trained using different data with various distributions, neural networks (NNs) tend to forget the previously learned knowledge. This phenomenon is often referred to as catastrophic forgetting. The catastrophic forgetting is an inevitable problem in continual learning models for dynamic environments. To address this issue, we propose a method, called continual Bayesian learning networks (CBLNs), which enables the networks to allocate additional resources to adapt to new tasks without forgetting the previously learned tasks. Using a Bayesian NN, CBLN maintains a mixture of Gaussian posterior distributions that are associated with different tasks. The proposed method tries to optimize the number of resources that are needed to learn each task and avoids an exponential increase in the number of resources that are involved in learning multiple tasks. The proposed method does not need to access the past training data and can choose suitable weights to classify the data points during the test time automatically based on an uncertainty criterion. We have evaluated the method on the MNIST and UCR time-series data sets. The evaluation results show that the method can address the catastrophic forgetting problem at a promising rate compared to the state-of-the-art models.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Posterior Meta-Replay for Continual Learning (2021, background): “CBLN, for instance, (Li et al., 2020) learns a separate Gaussian posterior approximation per task and later tries to merge similar posteriors in the induced Gaussian mixture model. Task inference is also done via predictive uncertainty although, for a more reliable estimation, all experiments consid”
MgSvF: Multi-Grained Slow versus Fast Framework for Few-Shot Class-Incremental Learning (2020, neutral): “Recently, there has been a large body of research in incremental learning [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43].”
A survey of uncertainty in deep neural networks (2021, background): “[118], [119], [120], theoretic advances in Bayesian learning [121] and beyond.”
Sequoia: A Software Framework to Unify Continual Learning Research (2021, background): “Contrarily, the latter, being on-policy, focuses on the current task and thus learns it faster, thereby increasing its online performance.”
Efficient Architecture Search for Continual Learning (2020, background): “To overcome such a dilemma, the second category is to expand the neural networks dynamically [5], [11]–[13].”
Organizing recurrent network dynamics by task-computation to enable continual learning (2020, methodology): “Previous approaches in this setting include regularisation of weight changes [13, 14], modiﬁcation to the learning signal [15–17], network architecture modiﬁcation [18, 19] or memory replay [20–22].”
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges (2020, methodology): “[388] introduced a technique termed as Continual Bayesian Learning Networks (CBLN) to address this problem that facilitates the networks to distribute supplementary resources to acclimatize to new tasks without forgetting the formerly learned tasks.”