High-Resolution Deep Convolutional Generative Adversarial Networks
Generative Adversarial Networks (GANs) convergence in a high-resolution setting with a computational constrain of GPU memory capacity (from 12GB to 24 GB) has been beset with difficulty due to the known lack of convergence rate stability. In order to boost network convergence of DCGAN (Deep Convolutional Generative Adversarial Networks) and achieve good-looking high-resolution results we propose a new layered network structure, HDCGAN, that incorporates current state-of-the-art techniques for this effect. A novel dataset, Curt\'o & Zarza, containing human faces from different ethnical groups in a wide variety of illumination conditions and image resolutions is introduced. Curt\'o is enhanced with HDCGAN synthetic images, thus being the first GAN augmented face dataset. We conduct extensive experiments on CelebA (MS-SSIM 0.1978 and Distance of Fr\'echet 8.77) and Curt\'o.
Code (1)
Tasks
GPUImage GenerationMS-SSIMSSIMVocal Bursts Intensity PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks
Recent advances in convolutional neural networks have shown promising results in 3D shape completion. But due to GPU memory limitations, these methods can only produce low-resolution outputs. To inpaint 3D models with se…
DecoderGenerative Adversarial NetworkGPUSuper-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning
Recent advances in Generative Adversarial Learning allow for new modalities of image super-resolution by learning low to high resolution mappings. In this paper we present our work using Generative Adversarial Networks (…
Image Super-ResolutionSuper-ResolutionPerformance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression
Image compression has been investigated for many decades. Recently, deep learning approaches have achieved a great success in many computer vision tasks, and are gradually used in image compression. In this paper, we dev…
Image CompressionSuper-ResolutionSuper-Resolution of 3D Micro-CT Images Using Generative Adversarial Networks: Enhancing Resolution and Segmentation Accuracy
We develop a procedure for substantially improving the quality of segmented 3D micro-Computed Tomography (micro-CT) images of rocks with a Machine Learning (ML) Generative Model. The proposed model enhances the resolutio…
Generative Adversarial NetworkSuper-ResolutionSource-Filter-Based Generative Adversarial Neural Vocoder for High Fidelity Speech Synthesis
This paper proposes a source-filter-based generative adversarial neural vocoder named SF-GAN, which achieves high-fidelity waveform generation from input acoustic features by introducing F0-based source excitation signal…
Speech Synthesistext-to-speechText to Speech