Summary of Cimgen: Controlled Image Manipulation by Finetuning Pretrained Generative Models on Limited Data, By Chandrakanth Gudavalli et al.
CIMGEN: Controlled Image Manipulation by Finetuning Pretrained Generative Models on Limited Data
by Chandrakanth Gudavalli, Erik Rosten, Lakshmanan Nataraj, Shivkumar Chandrasekaran, B. S. Manjunath
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a method for conditional image generation using semantic maps. This approach allows for flexible user controls in content creation and image editing by modifying the semantic map to insert, remove, or replace objects. The method leverages pre-trained GANs like CycleGAN or Pix2Pix GAN and fine-tunes them on a limited dataset of reference images associated with semantic maps. The technique demonstrates its capacity and possible applications in image forgery and editing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us create new images by changing what’s already there, like objects. Imagine having a map of the things in an image, and you can easily add or remove things on that map! This is useful for making fake pictures look real or for editing photos. The method uses special computer programs (GANs) to do this. It works by teaching these GANs to change images based on what’s in the map. The paper shows how well this works and why it matters. |
Keywords
* Artificial intelligence * Gan * Image generation
