Semi-supervised image attribute editing using generative adversarial networks

TL;DR: In this paper , a structural causal model (SCM) is used to generate counterfactual examples for an input, which can be used to evaluate bias of machine learning models, e.g., against specific demographic groups.

TL;DR: Wang et al. as mentioned in this paper proposed a Domain-guided Noise-optimization-based Inversion (DNI) method to perform facial image manipulation, which works based on an inverse code that includes a novel domain-guided encoder called Image2latent to project the image to StyleGAN2 latent space, which can reconstruct an input image with high-quality and maintain its semantic meaning well.

TL;DR: Yahya et al. as discussed by the authors proposed an iterative facial image inpainting method using the cyclic reverse generator (CRG) architecture, which provides an encoder-generator model.

TL;DR: Liu et al. as mentioned in this paper proposed Local and Global Perception Generative Adversarial Network (LGP-GAN) with a two-stage cascaded structure which is designed to extract and synthesize the details of the crucial facial regions.

TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.

TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.

TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.

TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.