In recent years, convolutional neural networks (CNNs) have gained momentum in machine learning and deep learning. CNNs are robust architectures for image classification and other tasks that involve complex visual data. One of the most critical aspects of CNNs is batch normalization (BN) networks. BN networks normalize CNN's inputs and outputs, which helps improve the overall accuracy and speed of the network. Batch normalization networks are used in CNNs to reduce the variance in the input data and help reduce the amount of noise in the data, leading to better accuracy results. BN networks also allow for faster training, reducing the need for hyperparameter tuning. Despite the advantages of BN networks, not all recent CNN architectures are using them. There are two main reasons for this. First, BN networks are computationally expensive and require memory, limiting the amount of data processing in a single pass and making it difficult to train more extensive networks. Second, BN networks need high tuning to achieve optimal results, which can be tedious and time-consuming, so some researchers refrain from using them. Despite the potential drawbacks, BN networks are the most popular CNN architectures. For example, they use batch normalization in the ResNet and VGG architectures for image classification and object detection tasks. In addition, BN networks are also used in the Inception and MobileNet architectures, both of which are popular for mobile applications. Overall, batch normalization networks can benefit CNN architectures but are only sometimes used due to their computational demands and high tuning required. While they can help improve a network's accuracy and speed, researchers must carefully evaluate their needs before deciding whether or not to use BN networks.

References:

1. Ioffe, Sergey, and Christian Szegedy. "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift." ArXiv Preprint ArXiv:1502.03167, 2015.

2. He, Kaiming, et al. "Deep Residual Learning for Image Recognition." ArXiv Preprint ArXiv:1512.03385, 2015.

3. Simonyan, Karen, and Andrew Zisserman. "Very Deep Convolutional Networks for Large-Scale Image Recognition." ArXiv Preprint ArXiv:1409.1556, 2014.

4. Szegedy, Christian, et al. "Going Deeper with Convolutions." ArXiv Preprint ArXiv:1409.4842, 2014.

5. Howard, Andrew G., et al. "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications." ArXiv Preprint ArXiv:1704.04861, 2017.

Thanks Felix M. Delmonte