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Contextual RNN-GANs for Abstract Reasoning Diagram Generation
A novel Context-RNN-GAN model for diagrammatic abstract reasoning and image generation -
MNIST and celebA
MNIST and celebA datasets were used to train and evaluate the proposed QDCGAN architecture. -
Iris-GAN: Learning to Generate Realistic Iris Images Using Convolutional GAN
Iris images generated using deep convolutional generative adversarial networks -
MNIST and CelebA datasets
The authors used MNIST and CelebA datasets for their experiments. -
Synthetic Data from Diffusion Models
Synthetic data from diffusion models augmenting real-world data -
RLDF ImageNet-100
Generated ImageNet-100 data for training ResNet-18 -
Toy 2D dataset
The dataset used in the paper is a toy dataset consisting of 2D images. -
Anime128x128 dataset
The dataset used in the paper is a Anime128x128 dataset. -
LSUN-Church and LSUN-Bedroom
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the LSUN-Church and LSUN-Bedroom datasets. -
DDIM and LDMs diffusion models
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used the DDIM diffusion model and the LDMs diffusion model. -
Self-Guided Generation of Minority Samples Using Diffusion Models
We present a novel approach for generating minority samples that live on low-density regions of a data manifold. -
SRNDiff: Short-term precipitation nowcasting with condition diffusion model
Diffusion models are widely used in image generation because they can generate high-quality and realistic samples. -
StyleFusion
StyleFusion: A Generative Model for Disentangling Spatial Segments -
YuruGAN: Yuru-Chara Mascot Generator Using Generative Adversarial Networks Wi...
A yuru-chara is a mascot character created by local governments and companies for publicizing information on areas and products. Because it takes various costs to create a... -
Visual ChatGPT
Visual ChatGPT is a system that integrates different Visual Foundation Models to understand visual information and generation corresponding answers. -
Stable Diffusion v2 release
Stable diffusion v2 release -
25-Gaussian
The data distribution is taken to be a 25-dimensional Gaussian distribution, generated by mixture of gaussians. -
Simple SDE
The data distribution is taken to be a one-dimensional Gaussian distribution, P0(x0) = N (x0 | 0, v0). -
ImageNet and CC-3M datasets
ImageNet [9] and CC-3M [43] datasets
