We use ReLU as the activation function.

We construct SEIDNet based on PyTorch1. There are 26 convolutional layers for extracting the visual feature map from the rainy image. The feature masking contains two convolutional layers. It computes the rain (or object) feature map. There is a pair of batch normalization and ReLU layers between the adjacent convolutional layers. The size of kernels in each convolutional layer is 3 3. Vid generates 3 3kernel for deraining each pixel.

In the supplementary material, we first introduce the pseudo algorithm of DA W . Then we clarify the Then, we provide a more detailed explanation of Figures 1, 2, 4, and 5, which are slightly abbreviated due to the limited space of the main paper. In the naive pseudo-labeling method, all pseudo labels are enrolled into training, i.e., E 1 + E 2, which is guaranteed by theoretical functional analysis in the next section. Inequality 45 holds true at all times. In this section, we provide more qualitative results between ours and other competitors.