https://github.com/labcontext/text-to-image-with-SNGAN-and-WGAN (=== almost same git ===) https://github.com/wooramkang/TEXT-TO-IMAGE-GANs-V.RESEARCH- (=== almost same git ===)

Papers for image caption and text to image GANs

papers i have red for image caption, image description

A. design neural networks and object detection

1. Going deeper with convolutions
2. Rethinking the Inception Architecture for Computer Vision
3. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
4. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
5. Deep Residual Learning for Image Recognition
6. Residual Networks are Exponential Ensembles of Relatively Shallow Networks
7. Speed/accuracy trade-offs for modern convolutional object detectors
8. Dropout: A simple way to prevent neural networks from overfitting

B. Viusal semantic embedding

1. Deep Visual-Semantic Alignments for Generating Image Descriptions
2. Order-embeddings of images and language
3. Unifying visual-semantic embeddings with multimodal neural language models
4. Multimodal convolutional neural networks for matching image and sentence

C. Image caption

1. Show and Tell: a neural image caption generator
2. Show, Adapt and Tell: Adversarial training of cross-domain image captioner
3. Show, Attend and Tell: Neural image caption generation with visual attention

D. Reinforcement learning

1. Deep Reinforcement learning-based image captioning with embedding reward

E. image segmentation

1. learning deconvolution network for semantic segmentation

F. unsupervised learning, Deep Generative Model

1. Building high-level features using large scale unsupervised learning
2. Auto-encoding variational bayes
3. Generative adversarial nets
4. Unsupervised representation learning with deep convolutional generative adversarial networks
5. DRAW: A recurrent neural network for image generation.
6. Pixel recurrent neural networks
7. Conditional image generation with PixelCNN decoders

G. image attention machanism

1. Show, Attend and Tell: Neural image caption generation with visual attention
2. Generating images from captions with attention
3. Self-Attention Generative Adversarial Networks
4. Bottom-up and top-down attention for image captioning and visual question answering
5. Generative Image Inpainting with Contextual Attention
6. Watch What You Just Said: Image Captioning with Text-Conditional Attention
7. Aligning where to see and what to tell: image caption with region-based attention and scene factorization
8. Self-Attention Generative Adversarial Networks

H. generative adversarial nets

1. generative-adversarial-nets
2. Unsupervised representation learning with deep convolutional generative adversarial networks
3. Least Squares Generative Adversarial Networks
4. Semi-Supervised Learning with Generative Adversarial Networks
5. Conditional Generative Adversarial Nets
6. Conditional Image Synthesis with Auxiliary Classifier GANs
7. Unpaired Image-to-Image Translation
8. StackGAN, Text to Photo-realistic Image Synthesis
9. Wang Stacked Conditional Generative CVPR_2018_paper
10. Mueller GANerated Hands for_CVPR_2018_paper
11. chang PairedCycleGAN Asymmetric Style CVPR_2018_paper
12. Finding Tiny Faces in the Wild with Generative Adversarial Network
13. Wei Person Transfer GAN CVPR_2018_paper