Papers Conditional Image Generation
“Conditional Image Generation” 태그가 달린 논문 327편 · 필터 해제
ArrowGAN : Learning to Generate Videos by Learning Arrow of Time
Training GANs on videos is even more sophisticated than on images because videos have a distinguished dimension: time. While recent methods designed a dedicated architecture considering time, generated videos are still f…
Conditional Image GenerationImage GenerationVideo GenerationGuiding GANs: How to control non-conditional pre-trained GANs for conditional image generation
Generative Adversarial Networks (GANs) are an arrange of two neural networks -- the generator and the discriminator -- that are jointly trained to generate artificial data, such as images, from random inputs. The quality…
Conditional Image GenerationImage GenerationSemantic SegmentationMVP: Multivariate polynomials for conditional generation
Conditional Generative Adversarial Nets (cGANs) have been widely adopted for image generation. cGANs take i) a noise vector and ii) a conditional variable as input. The conditional variable can be discrete (e.g., a clas…
Conditional Image GenerationImage GenerationImage-to-Image TranslationTranslationMembership Attacks on Conditional Generative Models Using Image Difficulty
Membership inference attacks (MIA) try to detect if data samples were used to train a Neural Network model. As training data is very valuable in machine learning, MIA can be used to detect the use of unauthorized data. U…
Conditional Image GenerationImage GenerationTranslationAdaptive Weighted Discriminator for Training Generative Adversarial Networks
Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning. A variety of discriminator loss functions have been developed to train GAN's di…
Conditional Image GenerationGenerative Adversarial NetworkImage GenerationUnconditional Image GenerationMPG: A Multi-ingredient Pizza Image Generator with Conditional StyleGANs
Multilabel conditional image generation is a challenging problem in computer vision. In this work we propose Multi-ingredient Pizza Generator (MPG), a conditional Generative Neural Network (GAN) framework for synthesizin…
Conditional Image GenerationImage GenerationOmni-GAN: On the Secrets of cGANs and Beyond
The conditional generative adversarial network (cGAN) is a powerful tool of generating high-quality images, but existing approaches mostly suffer unsatisfying performance or the risk of mode collapse. This paper presents…
Conditional Image GenerationGenerative Adversarial NetworkImage GenerationMichiGAN: Multi-Input-Conditioned Hair Image Generation for Portrait Editing
Despite the recent success of face image generation with GANs, conditional hair editing remains challenging due to the under-explored complexity of its geometry and appearance. In this paper, we present MichiGAN (Multi-I…
Conditional Image GenerationImage GenerationEvaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs
Chest x-rays are a vital tool in the workup of many patients. Similar to most medical imaging modalities, they are profoundly multi-modal and are capable of visualising a variety of combinations of conditions. There is a…
Conditional Image GenerationData AugmentationDiagnosticEthics+3TinyGAN: Distilling BigGAN for Conditional Image Generation
Generative Adversarial Networks (GANs) have become a powerful approach for generative image modeling. However, GANs are notorious for their training instability, especially on large-scale, complex datasets. While the rec…
Conditional Image GenerationImage GenerationKnowledge DistillationConditional Image Generation with One-Vs-All Classifier
This paper explores conditional image generation with a One-Vs-All classifier based on the Generative Adversarial Networks (GANs). Instead of the real/fake discriminator used in vanilla GANs, we propose to extend the dis…
AllConditional Image GenerationImage GenerationTreeGAN: Incorporating Class Hierarchy into Image Generation
Conditional image generation (CIG) is a widely studied problem in computer vision and machine learning. Given a class, CIG takes the name of this class as input and generates a set of images that belong to this class. In…
Conditional Image GenerationImage Generationnot-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution
State-of-the-art models for high-resolution image generation, such as BigGAN and VQVAE-2, require an incredible amount of compute resources and/or time (512 TPU-v3 cores) to train, putting them out of reach for the large…
Conditional Image GenerationDecoderImage GenerationImage Super-Resolution+1Causal Adversarial Network for Learning Conditional and Interventional Distributions
We propose a generative Causal Adversarial Network (CAN) for learning and sampling from conditional and interventional distributions. In contrast to the existing CausalGAN which requires the causal graph to be given, our…
Conditional Image GenerationFace GenerationImage GenerationText as Neural Operator: Image Manipulation by Text Instruction
In recent years, text-guided image manipulation has gained increasing attention in the multimedia and computer vision community. The input to conditional image generation has evolved from image-only to multimodality. In …
Conditional Image GenerationImage CaptioningImage GenerationImage Manipulation+1Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
We present a generic image-to-image translation framework, pixel2style2pixel (pSp). Our pSp framework is based on a novel encoder network that directly generates a series of style vectors which are fed into a pretrained …
Conditional Image GenerationFace GenerationImage-to-Image TranslationTranslationReconstructing the Noise Variance Manifold for Image Denoising
Deep Convolutional Neural Networks (CNNs) have been successfully used in many low-level vision problems like image denoising. Although the conditional image generation techniques have led to large improvements in this ta…
Conditional Image GenerationDenoisingImage DenoisingImage GenerationInstance Selection for GANs
Recent advances in Generative Adversarial Networks (GANs) have led to their widespread adoption for the purposes of generating high quality synthetic imagery. While capable of generating photo-realistic images, these mod…
Conditional Image GenerationControllable Image Synthesis via SegVAE
Flexible user controls are desirable for content creation and image editing. A semantic map is commonly used intermediate representation for conditional image generation. Compared to the operation on raw RGB pixels, the …
Conditional Image GenerationImage GenerationImage-to-Image TranslationObject+1Lessons Learned from the Training of GANs on Artificial Datasets
Generative Adversarial Networks (GANs) have made great progress in synthesizing realistic images in recent years. However, they are often trained on image datasets with either too few samples or too many classes belongin…
AttributeConditional Image GenerationImage Generation