Continual Classification Learning Using Generative Models

Abstract

Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences. Neural networks can learn multiple tasks when trained on them jointly, but cannot maintain performance on previously learned tasks when tasks are presented one at a time. This problem is called catastrophic forgetting. In this work, we propose a classification model that learns continuously from sequentially observed tasks, while preventing catastrophic forgetting. We build on the lifelong generative capabilities of [10] and extend it to the classification setting by deriving a new variational bound on the joint log likelihood, $\log p(x;y)$.

arXiv comment5 pages, 4 figures, under review in Continual learning Workshop NIPS 2018

References in corpus (9)

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• Continual Learning Through Synaptic Intelligence
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Cited by in corpus (10)

• A continual learning survey: Defying forgetting in classification tasks
• Lifelong Generative Modeling
• Online Continual Learning with Maximally Interfered Retrieval
• A Classification Supervised Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids
• Continual Learning in Neural Networks
• Automatic Recall Machines: Internal Replay, Continual Learning and the Brain
• Memory Efficient Class-Incremental Learning for Image Classification
• Rethinking Experience Replay: a Bag of Tricks for Continual Learning
• Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts
• MyMigrationBot: A Cloud-based Facebook Social Chatbot for Migrant Populations