Telefonaktiebolaget lm ericsson (publ) (20240095499). NOISE LEARNING-BASED DENOISING AUTOENCODER simplified abstract
Contents
- 1 NOISE LEARNING-BASED DENOISING AUTOENCODER
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 NOISE LEARNING-BASED DENOISING AUTOENCODER - 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 How does this technology compare to traditional denoising methods?
- 1.11 What are the limitations of this technology in real-world applications?
- 1.12 Original Abstract Submitted
NOISE LEARNING-BASED DENOISING AUTOENCODER
Organization Name
telefonaktiebolaget lm ericsson (publ)
Inventor(s)
NOISE LEARNING-BASED DENOISING AUTOENCODER - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095499 titled 'NOISE LEARNING-BASED DENOISING AUTOENCODER
Simplified Explanation
The patent application describes methods and apparatuses for noise learning-based denoising of noisy input data y, which is equal to the original data x plus the noise n (i.e., y=x+n). This approach differs from conventional denoising autoencoder (DAE) methods by learning the noise n in the noisy input data y and then regenerating the original data x by subtracting the learned noise n from y.
- Input noisy data y into an encoder of a neural network to learn the noise n.
- Output the learned noise n from a decoder of the neural network.
- Train the neural network by inputting noisy training data into the encoder and outputting training noise from the decoder.
Potential Applications
The technology can be applied in various fields such as image processing, audio signal denoising, video enhancement, and data compression.
Problems Solved
1. Eliminates noise from input data to improve data quality and accuracy. 2. Enhances the performance of machine learning models by providing cleaner input data.
Benefits
1. Improved data quality and accuracy. 2. Enhanced performance of machine learning models. 3. Efficient noise removal process.
Potential Commercial Applications
"Noise Learning-Based Denoising Technology: Applications and Benefits"
Possible Prior Art
There may be prior art related to denoising autoencoders and noise removal techniques in the field of signal processing and machine learning.
Unanswered Questions
How does this technology compare to traditional denoising methods?
This article does not provide a direct comparison between noise learning-based denoising and traditional denoising methods.
What are the limitations of this technology in real-world applications?
The article does not discuss any potential limitations or challenges that may arise when implementing this technology in real-world scenarios.
Original Abstract Submitted
methods and apparatuses for noise learning-based denoising of noisy input data y that is equal to the original data x plus the noise n (i.e., y=x+n). in contrast with a conventional denoising autoencoder (dae) method that attempts to learn the original data x directly from noisy input data y, the noise learning-based denoising learns the noise n in the noisy input data y and then regenerates the original data x by subtracting the learned noise n from the noisy input data y. learning the noise n may include inputting the noisy input data y into an encoder of a neural network, and the learned noise n may be output from a decoder of the neural network. training the neural network may include inputting noisy training data into an encoder of the neural network and outputting training noise from a decoder of the neural network.
