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Found 7 papers, 0 papers with code
Image Generation with Self Pixel-wise Normalization
Region-adaptive normalization (RAN) methods have been widely used in the generative adversarial network (GAN)-based image-to-image translation technique.
Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention
In addition, we propose an improved information aggregation module with PAKA, called the hierarchical PAKA module (HPM).
PConv: Simple yet Effective Convolutional Layer for Generative Adversarial Network
This paper presents a novel convolutional layer, called perturbed convolution (PConv), which focuses on achieving two goals simultaneously: improving the generative adversarial network (GAN) performance and alleviating the memorization problem in which the discriminator memorizes all images from a given dataset as training progresses.
Simple yet Effective Way for Improving the Performance of GAN
In adversarial learning, discriminator often fails to guide the generator successfully since it distinguishes between real and generated images using silly or non-robust features.
cGANs with Conditional Convolution Layer
Conditional generative adversarial networks (cGANs) have been widely researched to generate class conditional images using a single generator.
PEPSI++: Fast and Lightweight Network for Image Inpainting
To address this problem, we propose a novel network architecture called PEPSI: parallel extended-decoder path for semantic inpainting network, which aims at reducing the hardware costs and improving the inpainting performance.
Unsupervised Deep Contrast Enhancement with Power Constraint for OLED Displays
In the proposed method, the power consumption is constrained by simply reducing the brightness a certain ratio, whereas the perceived visual quality is preserved as much as possible by enhancing the contrast of the image using a convolutional neural network (CNN).
