过度拟合
计算机科学
卷积神经网络
人工智能
辍学(神经网络)
贝叶斯推理
推论
机器学习
稳健性(进化)
贝叶斯概率
伯努利原理
后验概率
人工神经网络
算法
工程类
基因
航空航天工程
生物化学
化学
作者
Yarin Gal,Zoubin Ghahramani
出处
期刊:Cornell University - arXiv
日期:2015-01-01
被引量:582
标识
DOI:10.48550/arxiv.1506.02158
摘要
Convolutional neural networks (CNNs) work well on large datasets. But labelled data is hard to collect, and in some applications larger amounts of data are not available. The problem then is how to use CNNs with small data -- as CNNs overfit quickly. We present an efficient Bayesian CNN, offering better robustness to over-fitting on small data than traditional approaches. This is by placing a probability distribution over the CNN's kernels. We approximate our model's intractable posterior with Bernoulli variational distributions, requiring no additional model parameters. On the theoretical side, we cast dropout network training as approximate inference in Bayesian neural networks. This allows us to implement our model using existing tools in deep learning with no increase in time complexity, while highlighting a negative result in the field. We show a considerable improvement in classification accuracy compared to standard techniques and improve on published state-of-the-art results for CIFAR-10.
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