Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework

Yong-Deok Kim; Taewoong Jang; Bohyung Han; Seungjin Choi

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arXiv · 1506.02565

Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework

Yong-Deok Kim·Taewoong Jang·Bohyung Han·Seungjin Choi

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Abstract

We propose a Bayesian evidence framework to facilitate transfer learning from pre-trained deep convolutional neural networks (CNNs). Our framework is formulated on top of a least squares SVM (LS-SVM) classifier, which is simple and fast in both training and testing, and achieves competitive performance in practice. The regularization parameters in LS-SVM is estimated automatically without grid search and cross-validation by maximizing evidence, which is a useful measure to select the best performing CNN out of multiple candidates for transfer learning; the evidence is optimized efficiently by employing Aitken's delta-squared process, which accelerates convergence of fixed point update. The proposed Bayesian evidence framework also provides a good solution to identify the best ensemble of heterogeneous CNNs through a greedy algorithm. Our Bayesian evidence framework for transfer learning is tested on 12 visual recognition datasets and illustrates the state-of-the-art performance consistently in terms of prediction accuracy and modeling efficiency.

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BibTeX RIS

Yong-Deok Kim, Taewoong Jang, Bohyung Han, Seungjin Choi. 2015-06-08. Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework. https://arxiv.org/abs/1506.02565

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