Stochastic Learning of Non-Conjugate Variational Posterior for Image Classification

• URL: http://arxiv.org/abs/2412.08951v1
• Date: Thu, 12 Dec 2024 05:33:23 GMT
• Title: Stochastic Learning of Non-Conjugate Variational Posterior for Image Classification
• Authors: Kart-Leong Lim,
• Abstract summary: Large scale Bayesian nonsparametric (BNP) learner can handle datasets with large class number and large training size at fractional cost. A more challenging problem is to consider large scale learning on non-conjugate posterior. We develop a novel approach based on the recently proposed variational-maximization (VMM) learner to allow large scale learning on non-conjugate posterior.
• Score: 0.65268245109828
• License:
• Abstract: Large scale Bayesian nonparametrics (BNP) learner such as stochastic variational inference (SVI) can handle datasets with large class number and large training size at fractional cost. Like its predecessor, SVI rely on the assumption of conjugate variational posterior to approximate the true posterior. A more challenging problem is to consider large scale learning on non-conjugate posterior. Recent works in this direction are mostly associated with using Monte Carlo methods for approximating the learner. However, these works are usually demonstrated on non-BNP related task and less complex models such as logistic regression, due to higher computational complexity. In order to overcome the issue faced by SVI, we develop a novel approach based on the recently proposed variational maximization-maximization (VMM) learner to allow large scale learning on non-conjugate posterior. Unlike SVI, our VMM learner does not require closed-form expression for the variational posterior expectatations. Our only requirement is that the variational posterior is differentiable. In order to ensure convergence in stochastic settings, SVI rely on decaying step-sizes to slow its learning. Inspired by SVI and Adam, we propose the novel use of decaying step-sizes on both gradient and ascent direction in our VMM to significantly improve its learning. We show that our proposed methods is compatible with ResNet features when applied to large class number datasets such as MIT67 and SUN397. Finally, we compare our proposed learner with several recent works such as deep clustering algorithms and showed we were able to produce on par or outperform the state-of-the-art methods in terms of clustering measures.

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