StackGAN | Text to Image Generation with Stacked Generative Adversarial Networks
Explained what is StackGAN?
Text to Image Generation with Stacked Generative Adversarial Networks
Practical Implementation: • StackGAN Implementation| Text to Image Gen...
Tensorflow implementation of StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
“Generative Adversarial Networks (GAN) is the most interesting idea in the last ten years in machine learning.” — Yann LeCun, Director, Facebook AI
IMplementation:
StackGAN: Text to Photo-Realistic Image Synthesis
Model Architecture of StackGAN
Preparation of Dataset
Implementation of Stage I of StackGAN
Implementation of Stage II of StackGAN
At the end of this video, you will have a working model of StackGAN research paper, generating photorealistic images from text. Moreover, you will have a knowledge of how to train StackGAN for your own dataset or problem statement.
Example:
(Text Input): The bird is black with green and has a very short beak
(Output — Generated photo-realistic images)
The model architecture of StackGAN consists of mainly the following components:
Embedding: Converts the input variable length text into a fixed length vector. we will be using a pre-trained character level embedding.
Conditioning Augmentation (CA)
Stage I Generator: Generates low resolution (64*64) images.
Stage I Discriminator
Residual Blocks
Stage II Generator: Generates high resolution (256*256) images.
Stage II Discriminator
Queries: [email protected]
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