Task-agnostic Continual Learning with Hybrid Probabilistic Models
This addresses the challenge of catastrophic forgetting in deep learning for real-world applications, though it appears incremental as it builds on existing generative and anomaly detection techniques.
The authors tackled the problem of continual learning without forgetting by proposing HCL, a hybrid generative-discriminative approach using normalizing flows, which demonstrated strong performance on benchmarks like split-MNIST, split-CIFAR, and SVHN-MNIST.
Learning new tasks continuously without forgetting on a constantly changing data distribution is essential for real-world problems but extremely challenging for modern deep learning. In this work we propose HCL, a Hybrid generative-discriminative approach to Continual Learning for classification. We model the distribution of each task and each class with a normalizing flow. The flow is used to learn the data distribution, perform classification, identify task changes, and avoid forgetting, all leveraging the invertibility and exact likelihood which are uniquely enabled by the normalizing flow model. We use the generative capabilities of the flow to avoid catastrophic forgetting through generative replay and a novel functional regularization technique. For task identification, we use state-of-the-art anomaly detection techniques based on measuring the typicality of the model's statistics. We demonstrate the strong performance of HCL on a range of continual learning benchmarks such as split-MNIST, split-CIFAR, and SVHN-MNIST.