Neural Network Classification Using Behavior-Based Data Clustering
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Solution Overview
Problem
Training neural networks requires significant memory, time, and computing resources, which can be improved.
Innovation Solution
A system that uses unsupervised learning to group neural network training data based on object location data, combining it with supervised learning to train neural networks, reducing the computational resources needed for inference and allowing for efficient behavior pattern recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are trained using traditional supervised learning methods, then classification accuracy can be achieved, but significant memory, time, and computing resources are consumed
Solution Approach 1:
The patent segments the training process into two distinct phases: unsupervised pre-training where neurons are grouped into clusters based on input data patterns, and supervised fine-tuning where the clustered structure is utilized. This segmentation allows the network to learn efficient representations without requiring extensive computational resources during the initial learning phase, thereby reducing overall computing resource consumption while maintaining classification accuracy.
Solution Approach 2:
The patent applies preliminary action by performing unsupervised clustering of neurons before supervised training. During this preliminary phase, neurons are automatically grouped based on their response patterns to input data, creating an optimized structural organization. This pre-organization reduces the computational burden during subsequent supervised training and inference, as the network already has an efficient architecture that requires fewer resources to operate.
2Reliability
If more computational resources are allocated for neural network training, then better model performance can be achieved, but training time increases
Solution Approach 1:
The training process is divided into unsupervised pre-training and supervised fine-tuning stages. The unsupervised stage quickly establishes neuron clusters without requiring intensive computation, while the supervised stage leverages this pre-organized structure for efficient fine-tuning. This segmentation reduces total training time while maintaining model performance by avoiding redundant computational operations.
Solution Approach 2:
The network performs self-service through automatic unsupervised clustering during pre-training, where neurons self-organize into functional groups based on their natural response patterns to input data. This self-organization eliminates the need for manual feature engineering or extensive hyperparameter tuning, reducing both training time and computational resource requirements while achieving reliable model performance.
3Adaptability or versatility
If traditional neural network architectures are used for behavior recognition, then comprehensive pattern detection is possible, but storage requirements increase
Solution Approach 1:
The patent merges multiple neuron responses into cluster-level representations, where neurons with similar response patterns are grouped together. This merging reduces the dimensionality of the stored data while preserving the essential behavioral patterns. The clustered structure allows comprehensive pattern detection through group-level analysis rather than individual neuron analysis, significantly reducing storage requirements while maintaining adaptability for behavior recognition tasks.
Solution Approach 2:
The patent changes the organizational parameter from individual neurons to neuron clusters. By transforming the representation from fine-grained (individual neurons) to coarse-grained (clusters of neurons), the system reduces storage footprint while maintaining the ability to detect complex behavior patterns. This parameter change allows the network to generalize better with less stored information, improving efficiency without sacrificing pattern detection capability.
Data Source
AI summary
Apparatuses, systems, and techniques to train neural networks to perform classification. In at least one embodiment, one or more neural networks are trained to perform classification based, at least in part, on grouping one or more sets of neural network training data according to behaviors of one or more objects within one or more images represented by the training data.