Why do we need batch normalization?

Batch normalization solves a major problem called internal covariate shift. It helps by making the data flowing between intermediate layers of the neural network look, this means you can use a higher learning rate. It has a regularizing effect which means you can often remove dropout.

When should a batch normalization be done?

Batch normalization may be used on the inputs to the layer before or after the activation function in the previous layer. It may be more appropriate after the activation function if for s-shaped functions like the hyperbolic tangent and logistic function.

Why does residual learning work?

In short (from my cellphone), it works because the gradient gets to every layer, with only a small number of layers in between it needs to differentiate through. If you pick a layer from the bottom of your stack of layers, it has a connection with the output layer which only goes through a couple of other layers.

Which problem do residual connections used in ResNets solve?

Residual Networks (ResNets) Thanks to the deeper layer representation of ResNets as pre-trained weights from this network can be used to solve multiple tasks. It’s not only limited to image classification but also can solve a wide range of problems on image segmentation, keypoint detection & object detection.

Why does ResNet work better?

Using ResNet has significantly enhanced the performance of neural networks with more layers and here is the plot of error\% when comparing it with neural networks with plain layers. Clearly, the difference is huge in the networks with 34 layers where ResNet-34 has much lower error\% as compared to plain-34.

Does ResNet use batch normalization?

To overcome this prob- lem, the ResNet incorporates skip-connections between layers (He et al., 2016a,b) and the batch-normalization (BN) normalizes the input of activation functions (Ioffe and Szegedy, 2015). These architectures enable an extreme deep neural network to be trained with high performance.