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Found 8 papers, 0 papers with code
Improving GANs with a Feature Cycling Generator
For instance, the proposed method improves Frechet inception distance (FID) of StyleGAN2 from 4. 89 to 3. 72 on the FFHQ dataset and from 6. 64 to 5. 57 on the LSUN Bed dataset.
Effective Shortcut Technique for GAN
For instance, the proposed method improves the FID and IS scores on the tiny-ImageNet dataset from 35. 13 to 27. 90 and 20. 23 to 23. 42, respectively.
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.
A Novel Generator with Auxiliary Branch for Improving GAN Performance
To alleviate this problem, this brief introduces a novel generator architecture that produces the image by combining features obtained through two different branches: the main and auxiliary branches.
Generative Convolution Layer for Image Generation
This paper introduces a novel convolution method, called generative convolution (GConv), which is simple yet effective for improving the generative adversarial network (GAN) performance.
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.
cGANs with Conditional Convolution Layer
Conditional generative adversarial networks (cGANs) have been widely researched to generate class conditional images using a single generator.
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).
