Part
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Part
01
Generative Adversarial Networks - Part One
Three additional research papers are "Generative Adversarial Network with Attention for Face Attribute Editing", "Eye In-Painting with Exemplar Generative Adversarial Networks", and "Learning Residual Images for Face Attribute Manipulation" by use of the GAN.
Generative Adversarial Network with Attention for Face Attribute Editing
Link
- Research here.
Results
- The research concludes that a spatial attention mechanism should be introduced into the GAN framework and thus creating a SaGAN method to ensure that face attribute editing is more accurate.
- The research paper results show that the use of spatial attention within the attribute specific region has been manipulated.
Pros and Cons
- One of the benefits of this is that it can help face recognition through data augmentation.
- Another benefit is that the proposed spatial attention to GAN can perform far much better than existing face attribute editing methods.
- Another benefit is that one is able to manipulate very specific attributes while keeping the rest of the irrelevant regions unchanged within a specific attribute region.
- This kind of spatial attention mechanism ensures the manipulation of attributes only within the attribute-specific regions while keeping the rest irrelevant regions unchanged.
- The research report does not give any cons but says there is a need for more research into general image editing tasks.
Timing Estimates
- The research paper does not give any timing estimates but says that there is a need for more research into general image editing tasks.
Eye In-Painting with Exemplar Generative Adversarial Networks
Link
- Research here.
Results
- Based on the Facebook research, while an image has some sort of identifying a feature, exemplar GAN provide a useful solution for image generation or in-painting.
- The research reveals that exemplar GAN can provide superior perceptual results as they can use identifying information stored in reference images or perceptual codes by incorporating them.
- The research report reveals that it was able to observe that the in-painting quality is sensitive to mask placement and size.
Pros and Cons
- One benefit of using exemplar GANs is that they are able to be extended to other tasks within computer vision and other domains since they are a general framework.
- The report reveals that by using exemplar GAN leads to superior results in other in-painting tasks such as filling in missing regions from a natural but uniquely identifiable scene.
- The report reveals that a generated eye color that more closely matches the reference image is achievable by assigning a higher-weighted loss to the eye color via iris tracking
- The research report shows that, in the future, there is a need to try more combinations of code-based and reference-based exemplars.
Timing Estimates
- The research paper does not give any timing estimates but says that there is a need for more research in the general future to try more combinations of code-based and reference-based exemplars.
Learning Residual Images for Face Attribute Manipulation by use of the GAN
Link
- Research here.
Results
- The research paper tackles the task of face attribute manipulation by using the Generative Adversarial Network.
- The research adopted the use of both residual image learning and dual learning to successfully manipulate face images while leaving most details in the attribute areas unchanged.
- The research was able to allow them to focus on attribute specific areas and then learn from each other through the image transformation networks.
Pros and Cons
- One of the research benefits was that this was able to manipulate the face attribute according to a given attribute value without manipulating the whole image.
- Another research benefit is that one is able to distinguish the generated images from real images by using the transformation and discriminative networks responsible for attribute manipulation.
- The research also reveals that residual images can be effectively learned and used for attribute manipulations as another benefit for this.
- The research reveals the by using a feed-forward Convolutional Neural Network (CNN) and combining it with Generative Adversarial Networks (GAN) one is able to transform the image.
- The only drawback is that this has to be used in combination with the feed-forward Convolutional Neural Network (CNN).
Timing Estimates
- The research paper does not give any timing estimates but says that the research on face attribute manipulation needs more future research.
Your Research Team Applied The Following Strategy
Our initial approach was to look for research articles and research papers detailing the use of Generative Adversarial Networks in use during the changing of facial attributes. We were able to find several research papers that talked about GAN. We then focused on those research reports that put into focus on realistically changing facial attributes by using GAN. With this, we were able to locate three research papers and provided them.