Bayesian Convolutional Neural Networks with Bernoulli Approximate VariationalInference

Source: Dev.to

Summary

Big image models usually need lots of labeled photos, but labels are hard to get. When you only have few examples, the model memorizes noise and fails on new pictures.

This work shows a way to make convolutional nets more steady with small data, by letting parts of the network behave like they might be wrong. Instead of forcing one fixed setting, it treats filters as guesses, switching them on and off during training so the net learns to be careful about overfitting.

The idea borrows a common training trick called dropout, and explains it as a form of Bayesian thinking — basically the model keeps track of what it’s unsure about. You get better results without adding extra parts or slowing things down, so teams can try it with tools they already use.

Tests show this approach gives better accuracy on standard image tasks, especially when examples are scarce. It’s a neat step toward models that learn well even when data is limited, and you can try it fast.

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Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference