Google llc (20240185032). ENCODING AND RECONSTRUCTING INPUTS USING NEURAL NETWORKS simplified abstract
Contents
ENCODING AND RECONSTRUCTING INPUTS USING NEURAL NETWORKS
Organization Name
Inventor(s)
Martin Abadi of Palo Alto CA (US)
David Godbe Andersen of Pittsburgh PA (US)
ENCODING AND RECONSTRUCTING INPUTS USING NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185032 titled 'ENCODING AND RECONSTRUCTING INPUTS USING NEURAL NETWORKS
Simplified Explanation
The patent application describes a system for training and using neural networks to encode inputs and reconstruct them, including an encoder neural network, a trusted decoder neural network, and an adversary decoder neural network.
- The encoder neural network processes a primary neural network input and a key input to generate an encoded representation of the primary input.
- The trusted decoder neural network generates a first estimated reconstruction of the primary input using the encoded representation and the key input.
- The adversary decoder neural network generates a second estimated reconstruction of the primary input using only the encoded representation.
- The encoder and trusted decoder neural networks are trained jointly and adversarially to the adversary decoder neural network.
- Potential Applications
This technology can be applied in image and video compression, data encryption, and information retrieval systems.
- Problems Solved
This technology solves the problem of securely encoding and reconstructing sensitive data using neural networks.
- Benefits
The benefits of this technology include improved data security, efficient data compression, and accurate data reconstruction.
- Potential Commercial Applications
Potential commercial applications of this technology include secure communication systems, data storage solutions, and image/video processing software.
- Possible Prior Art
One possible prior art for this technology could be the use of generative adversarial networks (GANs) for image generation and reconstruction tasks.
- What are the limitations of this technology?
One limitation of this technology is the computational complexity involved in training and using multiple neural networks simultaneously.
- How does this technology compare to traditional data encryption methods?
This technology offers a more flexible and adaptive approach to data encryption compared to traditional methods, as it can learn to encode and decode data without explicit instructions.
- Frequently Updated Research
One frequently updated research topic related to this technology is the development of more efficient and robust neural network architectures for encoding and reconstructing data in various applications.
Original Abstract Submitted
systems, methods, devices, and other techniques are described herein for training and using neural networks to encode inputs and to process encoded inputs, e.g., to reconstruct inputs from the encoded inputs. a neural network system can include an encoder neural network, a trusted decoder neural network, and an adversary decoder neural network. the encoder neural network processes a primary neural network input and a key input to generate an encoded representation of the primary neural network input. the trusted decoder neural network processes the encoded representation and the key input to generate a first estimated reconstruction of the primary neural network input. the adversary decoder neural network processes the encoded representation without the key input to generate a second estimated reconstruction of the primary neural network input. the encoder and trusted decoder neural networks can be trained jointly, and these networks trained adversarially to the adversary decoder neural network.
