**Depthwise Convolution** is a type of convolution where we apply a single convolutional filter for each input channel. In the regular 2D [convolution](https://paperswithcode.com/method/convolution) performed over multiple input channels, the filter is as deep as the input and lets us freely mix channels to generate each element in the output. In contrast, depthwise convolutions keep each channel separate. To summarize the steps, we:

1. Split the input and filter into channels.
2. We convolve each input with the respective filter.
3. We stack the convolved outputs together.

Image Credit: [Chi-Feng Wang](https://towardsdatascience.com/a-basic-introduction-to-separable-convolutions-b99ec3102728)

**Instance Normalization** (also known as contrast normalization) is a normalization layer where:

$$
    y_{tijk} =  \frac{x_{tijk} - \mu_{ti}}{\sqrt{\sigma_{ti}^2 + \epsilon}},
    \quad
    \mu_{ti} = \frac{1}{HW}\sum_{l=1}^W \sum_{m=1}^H x_{tilm},
    \quad
    \sigma_{ti}^2 = \frac{1}{HW}\sum_{l=1}^W \sum_{m=1}^H (x_{tilm} - \mu_{ti})^2.
$$

This prevents instance-specific mean and covariance shift simplifying the learning process. Intuitively, the normalization process allows to remove instance-specific contrast information from the content image in a task like image stylization, which simplifies generation.

Instance Normalization

Instance Normalization: The Missing Ingredient for Fast Stylization

Depthwise Convolution

**Faster R-CNN** is an object detection model that improves on [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) by utilising a region proposal network ([RPN](https://paperswithcode.com/method/rpn)) with the CNN model. The RPN shares full-image convolutional features with the detection network, enabling nearly cost-free region proposals. It is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) for detection. RPN and Fast [R-CNN](https://paperswithcode.com/method/r-cnn) are merged into a single network by sharing their convolutional features: the RPN component tells the unified network where to look.

As a whole, Faster R-CNN consists of two modules. The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions.

Year	2016
Data Source	CC BY-SA - https://paperswithcode.com