**PReLU-Net** is a type of convolutional neural network that utilises parameterized ReLUs for its activation function. It also uses a robust initialization scheme - afterwards known as [Kaiming Initialization](https://paperswithcode.com/method/he-initialization) - that accounts for non-linear activation functions.

Multi-layer neural networks traditionally use  dot products between the output vector of previous layer and the incoming weight vector as the input to activation function. The result of dot product is unbounded. To bound dot product and decrease the variance, **Cosine Normalization** uses cosine similarity or centered cosine similarity (Pearson Correlation Coefficient) instead of dot products in neural networks. 

Using cosine normalization, the output of a hidden unit is computed by:

$$o = f(net_{norm})= f(\cos \theta) = f(\frac{\vec{w} \cdot \vec{x}} {\left|\vec{w}\right|  \left|\vec{x}\right|})$$

where $net_{norm}$ is the normalized pre-activation,  $\vec{w}$ is the incoming weight vector and $\vec{x}$ is the input vector, ($\cdot$) indicates dot product, $f$ is nonlinear activation function. Cosine normalization bounds the pre-activation between -1 and 1.

Cosine Normalization

Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks

PReLU-Net

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

**Random Gaussian Blur** is an image data augmentation technique where we randomly blur the image using a Gaussian distribution.

Image Source: [Wikipedia](https://en.wikipedia.org/wiki/Gaussian_blur)

Source	Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com