Intel corporation (20240355111). DYNAMIC LAYER PARTITIONING FOR INCREMENTAL TRAINING OF NEURAL RADIANCE FIELDS simplified abstract
Contents
DYNAMIC LAYER PARTITIONING FOR INCREMENTAL TRAINING OF NEURAL RADIANCE FIELDS
Organization Name
Inventor(s)
Alexey M. Supikov of Santa Clara CA (US)
Ronald Tadao Azuma of San Jose CA (US)
DYNAMIC LAYER PARTITIONING FOR INCREMENTAL TRAINING OF NEURAL RADIANCE FIELDS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240355111 titled 'DYNAMIC LAYER PARTITIONING FOR INCREMENTAL TRAINING OF NEURAL RADIANCE FIELDS
The abstract of this patent application describes an apparatus designed to train a neural network using initial video frames from an input video to generate neural representations of those frames. The neural network consists of two groups of layers, with the first group being retrained for subsequent video frames after the initial ones, while the second group is selectively frozen.
- The apparatus selects a layer from the frozen second group to be unfrozen for the first subsequent video frame, and then retrains both the first group of layers and the selected layer to generate a neural representation of that frame.
- The unselected layers of the second group remain frozen in the neural representation of the frame.
Potential Applications: - Video processing and analysis - Image recognition and classification - Autonomous driving systems - Surveillance and security systems - Medical imaging analysis
Problems Solved: - Efficient training of neural networks on video data - Improved accuracy in generating neural representations of video frames - Optimization of computational resources by selectively freezing layers
Benefits: - Enhanced performance in video analysis tasks - Faster training of neural networks on video data - Reduction in computational costs for processing video frames
Commercial Applications: This technology could be utilized in industries such as: - Entertainment (video editing, special effects) - Healthcare (medical imaging analysis) - Automotive (autonomous driving systems) - Security (surveillance and monitoring)
Questions about the technology: 1. How does this apparatus improve the efficiency of training neural networks on video data? 2. What are the potential implications of selectively freezing layers in the neural network for subsequent video frames?
Original Abstract Submitted
example apparatus disclosed herein are to train a neural network based on initial video frames of an input video to generate neural representations of the initial video frames, the neural network having a first group of layers and a second group of layers, the first group of layers to be retrained for subsequent video frames after the initial video frames, the second group of layers to be selectively frozen for the subsequent video frames. disclosed example apparatus are also to select a layer of the second group of layers to be unfrozen for a first video frame subsequent to the initial video frames, and retrain the first group of layers and the selected layer of the second group of layers to generate a neural representation of the first video frame, unselected ones of the second group of layers to remain frozen in the neural representation of the first video frame.
