Qualcomm incorporated (20240214578). REGULARIZING NEURAL NETWORKS WITH DATA QUANTIZATION USING EXPONENTIAL FAMILY PRIORS simplified abstract
Contents
REGULARIZING NEURAL NETWORKS WITH DATA QUANTIZATION USING EXPONENTIAL FAMILY PRIORS
Organization Name
Inventor(s)
Thomas Alexander Ryder of San Diego CA (US)
Muhammed Zeyd Coban of Carlsbad CA (US)
Marta Karczewicz of San Diego CA (US)
REGULARIZING NEURAL NETWORKS WITH DATA QUANTIZATION USING EXPONENTIAL FAMILY PRIORS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240214578 titled 'REGULARIZING NEURAL NETWORKS WITH DATA QUANTIZATION USING EXPONENTIAL FAMILY PRIORS
Simplified Explanation: The patent application describes systems and techniques for processing video data using a neural network-based video encoder with quantization steps and an exponential-family prior.
- Neural network-based video encoder processes video data with quantization steps.
- Exponential-family prior is applied to the output of the first layer of the encoder.
- Total loss value is generated based on the loss value and the first layer output evaluation.
- The encoder is trained based on the total loss value.
Key Features and Innovation:
- Utilization of a neural network-based video encoder for processing video data.
- Incorporation of quantization steps in the video encoding process.
- Application of an exponential-family prior to enhance the output evaluation.
- Training the encoder based on the total loss value to improve performance.
- Overall, the innovation focuses on optimizing video data processing using advanced techniques.
Potential Applications: The technology can be applied in various fields such as video streaming, surveillance systems, video editing software, and virtual reality applications.
Problems Solved:
- Efficient processing of video data.
- Improved video quality through advanced encoding techniques.
- Enhanced training methods for neural network-based video encoders.
Benefits:
- Higher quality video output.
- Faster video processing speeds.
- Enhanced performance of video encoding systems.
- Improved training processes for neural networks.
Commercial Applications: The technology can be utilized in industries such as entertainment, security, education, and telecommunications for enhancing video processing capabilities and improving overall user experience.
Prior Art: Researchers can explore prior art related to neural network-based video encoders, video processing techniques, and quantization methods in the field of video encoding.
Frequently Updated Research: Stay updated on advancements in neural network-based video encoding, video processing algorithms, and training methods for improving video quality and efficiency.
Questions about Video Data Processing: 1. What are the key benefits of using a neural network-based video encoder for processing video data? 2. How does the application of an exponential-family prior enhance the output evaluation in video encoding systems?
Original Abstract Submitted
systems and techniques are described for processing video data. for instance, a process can include processing a frame of video data using a first layer of a neural network-based video encoder, the neural network-based video encoder performing at least one quantization step. the process can further include applying an exponential-family prior to an output of the first layer of the neural network-based video encoder to generate a first layer output evaluation, generating a total loss value for the neural network-based video encoder based on a sum of a loss value for the neural network-based video encoder and the first layer output evaluation, and training the neural network-based video encoder based on the total loss value.
