Sequential Function-Space Variational Inference via Gaussian Mixture Approximation

• URL: http://arxiv.org/abs/2503.07114v1
• Date: Mon, 10 Mar 2025 09:38:35 GMT
• Title: Sequential Function-Space Variational Inference via Gaussian Mixture Approximation
• Authors: Menghao Waiyan William Zhu, Pengcheng Hao, Ercan Engin Kuruoğlu,
• Abstract summary: Sequential function-space variational inference (SFSVI) is a continual learning method based on variational inference.<n>We propose an SFSVI method which uses a Gaussian mixture variational distribution.<n>We find that in terms of final average accuracy, Gaussian mixture methods perform better than Gaussian methods and likelihood-focused methods perform better than prior-focused methods.
• Score: 0.6827423171182154
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continual learning is learning from a sequence of tasks with the aim of learning new tasks without forgetting old tasks. Sequential function-space variational inference (SFSVI) is a continual learning method based on variational inference which uses a Gaussian variational distribution to approximate the distribution of the outputs of a finite number of selected inducing points. Since the posterior distribution of a neural network is multi-modal, a Gaussian distribution could only match one mode of the posterior distribution, and a Gaussian mixture distribution could be used to better approximate the posterior distribution. We propose an SFSVI method which uses a Gaussian mixture variational distribution. We also compare different types of variational inference methods with and without a fixed pre-trained feature extractor. We find that in terms of final average accuracy, Gaussian mixture methods perform better than Gaussian methods and likelihood-focused methods perform better than prior-focused methods.

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