Normal Similarity Network for Generative Modelling
This work addresses the under-explored applicability of Gaussian distributions in deep networks for generative modeling, offering a new approach for computer vision tasks.
The paper tackles the problem of using Gaussian distributions in deep generative models by proposing the Normal Similarity Network (NSN), a novel deep generative model with Gaussian-style filters, and achieves encouraging results in image generation, styling, and reconstruction from occluded images across various computer vision applications.
Gaussian distributions are commonly used as a key building block in many generative models. However, their applicability has not been well explored in deep networks. In this paper, we propose a novel deep generative model named as Normal Similarity Network (NSN) where the layers are constructed with Gaussian-style filters. NSN is trained with a layer-wise non-parametric density estimation algorithm that iteratively down-samples the training images and captures the density of the down-sampled training images in the final layer. Additionally, we propose NSN-Gen for generating new samples from noise vectors by iteratively reconstructing feature maps in the hidden layers of NSN. Our experiments suggest encouraging results of the proposed model for a wide range of computer vision applications including image generation, styling and reconstruction from occluded images.