Papers Conditional Image Generation
“Conditional Image Generation” 태그가 달린 논문 327편 · 필터 해제
Hierarchical Text-Conditional Image Generation with CLIP Latents
Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior …
Conditional Image GenerationDecoderDiversityImage Generation+2DT2I: Dense Text-to-Image Generation from Region Descriptions
Despite astonishing progress, generating realistic images of complex scenes remains a challenging problem. Recently, layout-to-image synthesis approaches have attracted much interest by conditioning the generator on a li…
Conditional Image GenerationImage GenerationImage-text matchingText Matching+2IR-GAN: Image Manipulation with Linguistic Instruction by Increment Reasoning
Conditional image generation is an active research topic including text2image and image translation. Recently image manipulation with linguistic instruction brings new challenges of multimodal conditional generation. How…
Conditional Image GenerationGenerative Adversarial NetworkImage GenerationImage ManipulationDenoising Likelihood Score Matching for Conditional Score-based Data Generation
Many existing conditional score-based data generation methods utilize Bayes' theorem to decompose the gradients of a log posterior density into a mixture of scores. These methods facilitate the training procedure of cond…
Conditional Image GenerationImage GenerationSpatially Multi-conditional Image Generation
In most scenarios, conditional image generation can be thought of as an inversion of the image understanding process. Since generic image understanding involves solving multiple tasks, it is natural to aim at generating …
Conditional Image GenerationImage GenerationMissing LabelsTowards Device Efficient Conditional Image Generation
We present a novel algorithm to reduce tensor compute required by a conditional image generation autoencoder without sacrificing quality of photo-realistic image generation. Our method is device agnostic, and can optimiz…
Conditional Image GenerationCPUGPUImage GenerationAutoregressive Image Generation using Residual Quantization
For autoregressive (AR) modeling of high-resolution images, vector quantization (VQ) represents an image as a sequence of discrete codes. A short sequence length is important for an AR model to reduce its computational c…
Conditional Image GenerationImage GenerationImage ReconstructionQuantization+1Conditional Generative Data-free Knowledge Distillation
Knowledge distillation has made remarkable achievements in model compression. However, most existing methods require the original training data, which is usually unavailable due to privacy and security issues. In this pa…
Conditional Image GenerationData-free Knowledge DistillationImage GenerationKnowledge Distillation+1Transferring Unconditional to Conditional GANs with Hyper-Modulation
GANs have matured in recent years and are able to generate high-resolution, realistic images. However, the computational resources and the data required for the training of high-quality GANs are enormous, and the study o…
Conditional Image GenerationContrastive LearningTransfer LearningD2C: Diffusion-Decoding Models for Few-Shot Conditional Generation
Conditional generative models of high-dimensional images have many applications, but supervision signals from conditions to images can be expensive to acquire. This paper describes Diffusion-Decoding models with Contrast…
Conditional Image GenerationImage GenerationImage ManipulationSelf-Supervised LearningConditional Image Generation with Score-Based Diffusion Models
Score-based diffusion models have emerged as one of the most promising frameworks for deep generative modelling. In this work we conduct a systematic comparison and theoretical analysis of different approaches to learnin…
Conditional Image GenerationImage GenerationRebooting ACGAN: Auxiliary Classifier GANs with Stable Training
Conditional Generative Adversarial Networks (cGAN) generate realistic images by incorporating class information into GAN. While one of the most popular cGANs is an auxiliary classifier GAN with softmax cross-entropy loss…
Conditional Image GenerationDiversityGenerative Adversarial NetworkImage GenerationProjected GANs Converge Faster
Generative Adversarial Networks (GANs) produce high-quality images but are challenging to train. They need careful regularization, vast amounts of compute, and expensive hyper-parameter sweeps. We make significant headwa…
Conditional Image GenerationImage GenerationMulti-attribute Pizza Generator: Cross-domain Attribute Control with Conditional StyleGAN
Multi-attribute conditional image generation is a challenging problem in computervision. We propose Multi-attribute Pizza Generator (MPG), a conditional Generative Neural Network (GAN) framework for synthesizing images f…
AttributeConditional Image GenerationImage GenerationFlow Plugin Network for conditional generation
Generative models have gained many researchers' attention in the last years resulting in models such as StyleGAN for human face generation or PointFlow for the 3D point cloud generation. However, by default, we cannot co…
Conditional Image GenerationFace GenerationImage GenerationImage Manipulation+1ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
Climate change is a major threat to humanity, and the actions required to prevent its catastrophic consequences include changes in both policy-making and individual behaviour. However, taking action requires understandin…
Conditional Image GenerationDomain AdaptationImage GenerationUnsupervised Domain AdaptationFooDI-ML: a large multi-language dataset of food, drinks and groceries images and descriptions
In this paper we introduce the FooDI-ML dataset. This dataset contains over 1.5M unique images and over 9.5M store names, product names descriptions, and collection sections gathered from the Glovo application. The data …
Conditional Image GenerationImage GenerationImage RetrievalRetrievalGenerative Probabilistic Image Colorization
We propose Generative Probabilistic Image Colorization, a diffusion-based generative process that trains a sequence of probabilistic models to reverse each step of noise corruption. Given a line-drawing image as input, o…
ColorizationConditional Image GenerationImage ColorizationImage GenerationST-DDPM: Explore Class Clustering for Conditional Diffusion Probabilistic Models
Score-based generative models involve sequentially corrupting the data distribution with noise and then learns to recover the data distribution based on score matching. In this paper, for the diffusion probabilistic mode…
ClusteringConditional Image GenerationImage GenerationWeakly Supervised Keypoint Discovery
In this paper, we propose a method for keypoint discovery from a 2D image using image-level supervision. Recent works on unsupervised keypoint discovery reliably discover keypoints of aligned instances. However, when the…
Conditional Image GenerationImage GenerationKeypoint EstimationWeakly-supervised Learning