Zero-Shot Image Generation by Distilling Concepts from Multiple Captions
Accepted at Towards learning with limited labels Workshop, ICML 2018, Stockholm, Sweden. (Link)
Existing methods for generating an image from its description, use one single caption to generate a plausible image. A single caption by itself, would not be able to capture the variety of concepts that might be present in the image. We propose a generative model that will iteratively improve the concepts, and thereby the quality of the generated image by making use of multiple captions about a single image. This is achieved by ensuring `cross-caption cycle consistency' between the captions and the intermediate image representations. We report quantitative and qualitative results to bring out the efficacy of the proposed approach in zero-shot image generations, where images are generated from descriptions of novel classes that are not seen during training.
Architecture
Results
Code
Build on top of:
- Pytorch
- Python 2.7
Main File
- main.py
References
- StackGAN v2: Code