EDGAN

This repository modifies the original StackGAN code from github.

Dataset

use MSCOCO data set

get data set and preprocessed model

• Download MSCOCO dataset and annotations including captions and instances
• Download pretrained char-CNN-RNN embedding of MSCOCO.
• misc/preprocess_mscoco.py preprocess the image in to different sizes for selected supercategory ,write them into tfrecords file along with the corresponding caption embedding.

New features

Data input pipline

• use mscoco python API
• dataloader that load tfrecords from mscoco
• image augumentation including cropping, flipping, and standarlization (when downsample the image, use INTER_AREA method)
• sampling from multiple caption embeddings, visualize embedding distributions
• negative example (use inner product of embedding captions, see method CLSGAN)
• filter out selective images based on classes and their areas

Modification of GAN network

• enlarge capacity of generator network, adding 3 residual blocks.
• change relu to leaky relu
• option to no batch norm in discriminator
• increase or reduce discriminator final dimension

Multiple training methods of GAN

• Option to trian with vanilla GAN
• Option to train with WGAN (excluding weight clipping for batchnorm)
• Option to train with LSGAN
• Option to train with CLSGAN, continous least square GAN that estimates the inner products of embeddings between right caption embeddings and wrong caption embeddings.
• Option to train with BGAN (not implemented yet)

Classification Transfering from Imagenet to MSCOCO (for future 3 stage GAN)

• Label each image in MSCOCO with multiple labels for objects that have area larger than the threshold
• Transfer resnet from Caffe to Tensorflow
• Train resnet to classify the 80 categories of objects in MSCOCO

References publications

• StackGAN
• text2image
• [char-RNN-CNN]
• WGAN
• LSGAN
• BGAN