Monocular Depth Estimation Modification Using Pix2Pix Model with SELU and Alpha Dropout

This research focuses on modifying the Pix2Pix model for the purpose of depth estimation, which involves translating original images into RGB depth images. Depth estimation refers to the process of predicting the distance or depth information of objects in an image or scene. By incorporating the SELU activation function and employing the Alpha Dropout technique, we introduce modifications to the Pix2Pix model. The experimental results demonstrate that these modifications lead to a notable reduction in the discriminator loss by 0.09363725 and a decrease in the generator loss by 0.06176615 during the 14th iteration. These findings indicate a significant improvement in the performance of the Pix2Pix model for depth estimation after the applied modifications.