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Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference

arXiv 1506.02158 ↗First posted 6 Jun 2015stat.ML cs.LGcited by 159

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Abstract (arXiv)

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.

Key facts. Cited by 159 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2015.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 44; 2022: 36; 2023: 33; 2024: 22; 2025: 15; 2026: 8.

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