Nec corporation (20240135696). MODEL TRAINING APPARATUS, CONTROL METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM simplified abstract
MODEL TRAINING APPARATUS, CONTROL METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM
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MODEL TRAINING APPARATUS, CONTROL METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135696 titled 'MODEL TRAINING APPARATUS, CONTROL METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM
Simplified Explanation
The model training apparatus described in the patent application trains an image conversion model to generate an output image representing a scene in a second environment from an input image representing a scene in a first environment. The training process involves inputting a training image to the image conversion model to obtain feature maps, computing a patch-wise loss using features from positive and negative example patches, and training the model based on this loss.
- Image conversion model training apparatus:
* Trains model to convert images from one environment to another * Utilizes feature maps and patch-wise loss for training
- Potential Applications:**
This technology could be applied in various fields such as virtual reality, video games, and image editing software to seamlessly convert images from one environment to another.
- Problems Solved:**
This technology solves the problem of generating realistic output images representing scenes in different environments, improving the quality and efficiency of image conversion processes.
- Benefits:**
- Enhanced image conversion accuracy - Faster and more efficient training process - Versatile applications in different industries
- Potential Commercial Applications:**
- Enhancing Image Conversion in Virtual Reality and Video Games*
- Possible Prior Art:**
One possible prior art could be the use of generative adversarial networks (GANs) for image-to-image translation tasks, which have been used in similar applications in the past.
- Unanswered Questions:**
- 1. How does this technology compare to existing image conversion methods in terms of accuracy and efficiency?**
- Answer: This article does not provide a direct comparison with existing methods, so it is unclear how this technology stacks up against current practices.
- 2. Are there any limitations or constraints in the training process of the image conversion model described in the patent application?**
- Answer: The article does not mention any potential limitations or constraints that may arise during the training process, leaving this aspect unaddressed.
Original Abstract Submitted
the model training apparatus trains an image conversion model to generate, from an input image representing a scene in a first environment, an output image representing the scene in a second environment. the model training apparatus inputs a training image to the image conversion model to obtain a first feature map and an output image, input the output image to the image conversion model to obtain a second feature map, computes a patch-wise loss using the features corresponding to a positive example patch and a negative example patch extracted from the training image and a positive example patch extracted from the output image, and trains the image conversion model based on the patch-wise loss, which is extracted intensively from the region representing an object of a specific type.
