18485174. DEVICE AND METHOD FOR DETERMINING AN ENCODER CONFIGURED IMAGE ANALYSIS simplified abstract (Robert Bosch GmbH)
Contents
- 1 DEVICE AND METHOD FOR DETERMINING AN ENCODER CONFIGURED IMAGE ANALYSIS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DEVICE AND METHOD FOR DETERMINING AN ENCODER CONFIGURED IMAGE ANALYSIS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
DEVICE AND METHOD FOR DETERMINING AN ENCODER CONFIGURED IMAGE ANALYSIS
Organization Name
Inventor(s)
Anna Khoreva of Stuttgart (DE)
DEVICE AND METHOD FOR DETERMINING AN ENCODER CONFIGURED IMAGE ANALYSIS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18485174 titled 'DEVICE AND METHOD FOR DETERMINING AN ENCODER CONFIGURED IMAGE ANALYSIS
Simplified Explanation
The abstract describes a computer-implemented method for training an encoder to determine a latent representation of an image. The encoder is trained by providing a training image, determining a latent representation and a noise image, masking out parts of the noise image, generating a predicted image using a generative adversarial network, and adapting the encoder parameters based on a loss value that compares the predicted image with the training image.
- Encoder trained to determine latent representation of an image
- Training process includes providing a training image, determining latent representation and noise image, masking out parts of the noise image, generating a predicted image, and adapting encoder parameters based on loss value
Potential Applications
This technology can be applied in:
- Image processing
- Computer vision
- Machine learning
Problems Solved
This technology helps in:
- Improving image representation accuracy
- Enhancing image generation capabilities
- Optimizing encoder training process
Benefits
The benefits of this technology include:
- Higher quality image representations
- Improved training efficiency
- Enhanced image generation results
Potential Commercial Applications
The potential commercial applications of this technology include:
- Image editing software
- Automated image analysis tools
- Content generation platforms
Possible Prior Art
One possible prior art for this technology could be:
- Existing encoder training methods in machine learning
Unanswered Questions
How does this technology compare to traditional encoder training methods?
This article does not provide a direct comparison between this technology and traditional encoder training methods. It would be helpful to understand the specific advantages or improvements offered by this new approach.
What are the specific parameters used to adapt the encoder during training?
The article does not delve into the specific parameters or algorithms used to adapt the encoder based on the loss value. Understanding this aspect could provide insights into the effectiveness and efficiency of the training process.
Original Abstract Submitted
A computer-implemented method for training an encoder. The encoder is configured for determining a latent representation of an image. Training the encoder includes: determining a latent representation and a noise image by providing a training image to the encoder, wherein the encoder is configured for determining a latent representation and a noise image for a provided image; masking out parts of the noise image, thereby determining a masked noise image; determining a predicted image by providing the latent representation and the masked noise image to a generator of a generative adversarial network; training the encoder by adapting parameters of the encoder based on a loss value, wherein the loss value characterizes a difference between the predicted image and the training image.
