Samsung electronics co., ltd. (20240256864). HETEROGENEOUS PRODUCT AUTOENCODERS FOR CHANNEL-ADAPTIVE NEURAL CODES OF LARGE DIMENSIONS simplified abstract
Contents
HETEROGENEOUS PRODUCT AUTOENCODERS FOR CHANNEL-ADAPTIVE NEURAL CODES OF LARGE DIMENSIONS
Organization Name
Inventor(s)
Mohammad Vahid Jamali of Nashville TN (US)
Hamid Saber of San Diego CA (US)
Homayoon Hatami of San Diego CA (US)
Jung Hyun Bae of San Diego CA (US)
HETEROGENEOUS PRODUCT AUTOENCODERS FOR CHANNEL-ADAPTIVE NEURAL CODES OF LARGE DIMENSIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240256864 titled 'HETEROGENEOUS PRODUCT AUTOENCODERS FOR CHANNEL-ADAPTIVE NEURAL CODES OF LARGE DIMENSIONS
Simplified Explanation: The patent application describes a method of training an autoencoder using encoder and decoder neural networks. The method involves training the encoder neural networks with fixed weights of the decoder neural networks and iteratively training the decoder neural networks with different parameters.
- The method trains an autoencoder with encoder and decoder neural networks.
- The encoder neural networks are trained with fixed weights of the decoder neural networks.
- The decoder neural networks are iteratively trained with different parameters for each iteration.
- Pairs of decoder neural networks are replaced with new pairs during training.
- The method aims to improve the performance of the autoencoder by optimizing the training process.
Potential Applications: 1. Image and video compression. 2. Anomaly detection in data. 3. Feature extraction in machine learning models.
Problems Solved: 1. Enhancing the efficiency and accuracy of autoencoder training. 2. Improving the performance of neural networks in various applications. 3. Optimizing the process of feature extraction and data compression.
Benefits: 1. Faster and more effective training of autoencoders. 2. Enhanced performance of neural networks in data processing tasks. 3. Improved accuracy in anomaly detection and feature extraction.
Commercial Applications: Optimizing autoencoder training for industries such as: 1. Data analytics. 2. Image and video processing. 3. Machine learning model development.
Questions about Autoencoder Training: 1. How does the method of training encoder and decoder neural networks improve autoencoder performance? 2. What are the potential limitations of this training method in real-world applications?
Frequently Updated Research: Stay updated on advancements in autoencoder training techniques and neural network optimization for improved performance.
Original Abstract Submitted
a method of training an autoencoder that includes encoder neural networks and decoder neural networks. the method includes training the encoder neural networks in which weights of the decoder neural networks are fixed. the method also includes iteratively training the decoder neural networks for a number of iterations. for each iteration of the training of the decoder neural networks, a pair of decoder neural networks is replaced by another pair of neural networks, and a second decoder neural network of the pair of decoder neural networks utilizes different parameters than a first decoder neural network of the pair of decoder neural networks.
