Papers Conditional Image Generation
“Conditional Image Generation” 태그가 달린 논문 327편 · 필터 해제
MSGDD-cGAN: Multi-Scale Gradients Dual Discriminator Conditional Generative Adversarial Network
Conditional Generative Adversarial Networks (cGANs) have been used in many image processing tasks. However, they still have serious problems maintaining the balance between conditioning the output on the input and creati…
Conditional Image GenerationGenerative Adversarial NetworkImage GenerationMedical Image SegmentationInstance-Conditioned GAN
Generative Adversarial Networks (GANs) can generate near photo realistic images in narrow domains such as human faces. Yet, modeling complex distributions of datasets such as ImageNet and COCO-Stuff remains challenging i…
Conditional Image GenerationImage GenerationUnconditional Image GenerationIs Attention Better Than Matrix Decomposition?
As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global correlation discovery. However, is hand-crafted attention irreplaceable when modeling t…
Conditional Image GenerationImage GenerationSemantic SegmentationDual Projection Generative Adversarial Networks for Conditional Image Generation
Conditional Generative Adversarial Networks (cGANs) extend the standard unconditional GAN framework to learning joint data-label distributions from samples, and have been established as powerful generative models capable…
Conditional Image GenerationImage GenerationCIGLI: Conditional Image Generation from Language & Image
Multi-modal generation has been widely explored in recent years. Current research directions involve generating text based on an image or vice versa. In this paper, we propose a new task called CIGLI: Conditional Image G…
Conditional Image GenerationImage GenerationFlying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation
Lacking the ability to sense ambient environments effectively, blind and visually impaired people (BVIP) face difficulty in walking outdoors, especially in urban areas. Therefore, tools for assisting BVIP are of great im…
Conditional Image GenerationSegmentationSemantic SegmentationConditional GANs with Auxiliary Discriminative Classifier
Conditional generative models aim to learn the underlying joint distribution of data and labels to achieve conditional data generation. Among them, the auxiliary classifier generative adversarial network (AC-GAN) has bee…
Conditional Image GenerationDiversityGenerative Adversarial NetworkEstablishing process-structure linkages using Generative Adversarial Networks
The microstructure of material strongly influences its mechanical properties and the microstructure itself is influenced by the processing conditions. Thus, establishing a Process-Structure-Property relationship is a cru…
Conditional Image GenerationFeature EngineeringGenerative Adversarial NetworkGrid Partitioned Attention: Efficient TransformerApproximation with Inductive Bias for High Resolution Detail Generation
Attention is a general reasoning mechanism than can flexibly deal with image information, but its memory requirements had made it so far impractical for high resolution image generation. We present Grid Partitioned Atten…
Conditional Image GenerationDeep AttentionImage GenerationInductive BiasOn The Distribution of Penultimate Activations of Classification Networks
This paper studies probability distributions of penultimate activations of classification networks. We show that, when a classification network is trained with the cross-entropy loss, its final classification layer forms…
ClassificationConditional Image GenerationImage GenerationKnowledge DistillationD2C: Diffusion-Denoising 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 GenerationDenoisingImage GenerationImage Manipulation+1Diffusion Models Beat GANs on Image Synthesis
We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a serie…
Conditional Image GenerationDiversityImage GenerationImage Super-Resolution via Iterative Refinement
We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models to conditional image generation and performs super-resolution through a stochastic denois…
Conditional Image GenerationDenoisingImage GenerationImage Super-Resolution+1CoPE: Conditional image generation using Polynomial Expansions
Generative modeling has evolved to a notable field of machine learning. Deep polynomial neural networks (PNNs) have demonstrated impressive results in unsupervised image generation, where the task is to map an input vect…
AttributeConditional Image GenerationImage GenerationImage-to-Image Translation+3Conditional Image Generation by Conditioning Variational Auto-Encoders
We present a conditional variational auto-encoder (VAE) which, to avoid the substantial cost of training from scratch, uses an architecture and training objective capable of leveraging a foundation model in the form of a…
Conditional Image GenerationExperimental DesignImage GenerationImage InpaintingSandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity
We present Sandwich Batch Normalization (SaBN), a frustratingly easy improvement of Batch Normalization (BN) with only a few lines of code changes. SaBN is motivated by addressing the inherent feature distribution hetero…
Adversarial DefenseConditional Image GenerationImage GenerationNeural Architecture Search+1Kanerva++: extending The Kanerva Machine with differentiable, locally block allocated latent memory
Episodic and semantic memory are critical components of the human memory model. The theory of complementary learning systems (McClelland et al., 1995) suggests that the compressed representation produced by a serial even…
Conditional Image GenerationImage GenerationEfficient Conditional GAN Transfer with Knowledge Propagation across Classes
Generative adversarial networks (GANs) have shown impressive results in both unconditional and conditional image generation. In recent literature, it is shown that pre-trained GANs, on a different dataset, can be transfe…
Conditional Image GenerationImage GenerationTransfer LearningNegative Data Augmentation
Data augmentation is often used to enlarge datasets with synthetic samples generated in accordance with the underlying data distribution. To enable a wider range of augmentations, we explore negative data augmentation st…
Action RecognitionAnomaly DetectionConditional Image GenerationContrastive Learning+9Adversarial Text-to-Image Synthesis: A Review
With the advent of generative adversarial networks, synthesizing images from textual descriptions has recently become an active research area. It is a flexible and intuitive way for conditional image generation with sign…
Adversarial TextConditional Image GenerationDiversityImage Generation