Instance Map based Image Synthesis with a Denoising Generative Adversarial Network
Semantic layouts based Image synthesizing, which has benefited from the success of Generative Adversarial Network (GAN), has drawn much attention in these days. How to enhance the synthesis image equality while keeping the stochasticity of the GAN is still a challenge. We propose a novel denoising framework to handle this problem. The overlapped objects generation is another challenging task when synthesizing images from a semantic layout to a realistic RGB photo. To overcome this deficiency, we include a one-hot semantic label map to force the generator paying more attention on the overlapped objects generation. Furthermore, we improve the loss function of the discriminator by considering perturb loss and cascade layer loss to guide the generation process. We applied our methods on the Cityscapes, Facades and NYU datasets and demonstrate the image generation ability of our model.
Code (0)
등록된 구현이 없습니다.
Tasks
DenoisingGenerative Adversarial NetworkImage GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs
Denoising diffusion probabilistic models (DDPMs) are expressive generative models that have been used to solve a variety of speech synthesis problems. However, because of their high sampling costs, DDPMs are difficult to…
DenoisingSpeech Synthesistext-to-speechText to SpeechA Generative Adversarial Approach To ECG Synthesis And Denoising
Generative Adversarial Networks (GAN) are known to produce synthetic data that are difficult to discern from real ones by humans. In this paper we present an approach to use GAN to produce realistically looking ECG signa…
DenoisingTransfer LearningAmbient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data
It is widely accepted that medical imaging systems should be objectively assessed via task-based image quality (IQ) measures that ideally account for all sources of randomness in the measured image data, including the va…
Computed Tomography (CT)DenoisingGenerative Adversarial NetworkImage GenerationDeep Generative Models for 3D Medical Image Synthesis
Deep generative modeling has emerged as a powerful tool for synthesizing realistic medical images, driving advances in medical image analysis, disease diagnosis, and treatment planning. This chapter explores various deep…
DenoisingDiversityImage GenerationImage Reconstruction+2Dual Contradistinctive Generative Autoencoder
We present a new generative autoencoder model with dual contradistinctive losses to improve generative autoencoder that performs simultaneous inference (reconstruction) and synthesis (sampling). Our model, named dual con…
Image GenerationImage ReconstructionRepresentation Learning