Edge Node Routing for Unknown Object Classification
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Solution Overview
Problem
Machine learning models struggle to classify unknown objects or samples due to their limited training data, leading to incorrect labeling and discarding of images that cannot be properly classified, as they are not trained with data corresponding to certain classes, resulting in inefficient data processing and inaccurate inferences.
Innovation Solution
The system employs a framework that generates soft labels for samples by orchestrating a soft labeling process across edge nodes, managing and controlling heterogeneous and open set models, allowing difficult samples to be labeled or classified by other nodes with different sets of classes, and enabling manual labeling when necessary, using autoencoders and auto-classifiers to identify and classify unknown samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a machine learning model is trained with limited classes, then the model can be trained efficiently with available data, but the model cannot classify unknown objects or samples from classes not in the training set
Solution Approach 1:
The system dynamically routes samples to different nodes based on their classification capability. Edge nodes can be added or removed from the network, and the system adapts its classification strategy in real-time by selecting appropriate nodes for each sample, making the overall system both efficient and versatile
Solution Approach 2:
The classification system is designed as a universal framework where multiple edge nodes with different class sets work together. Each node maintains its specialized training efficiency while the collective system achieves universal classification capability across all classes present in any node's vocabulary
2Productivity
If a model classifies all samples using a fixed set of classes, then the processing is simple and fast, but unknown samples are misclassified or discarded
Solution Approach 1:
The system introduces an intermediary routing mechanism that directs samples to appropriate edge nodes. This mediator layer enables fast processing by routing to specialized nodes while ensuring reliable classification by matching samples with nodes that have the appropriate class vocabulary
Solution Approach 2:
The classification task is segmented across multiple edge nodes, each handling specific classes. This segmentation allows parallel processing for speed while ensuring that each sample is handled by a node specialized in its class, improving accuracy
3Reliability
If difficult samples are discarded when cannot be classified, then the system maintains high confidence in its classifications, but valuable data is lost and labeling efficiency decreases
Solution Approach 1:
The system merges the capabilities of multiple edge nodes into a collective classification system. Samples that are difficult for one node can be handled by another node with different class expertise, combining their strengths to maintain high confidence while processing all samples efficiently
Solution Approach 2:
The system implements feedback mechanisms where classification results from multiple nodes are aggregated and used to improve overall system performance. This feedback loop allows the system to learn from difficult samples and improve future classifications without discarding valuable data
Data Source
AI summary
Classifying unknown samples for scalable automatic labeling are disclosed. Unknown samples are soft labeled at edge nodes. When a node cannot soft label a sample, a candidate node is selected. The candidate node is selected based on why the sample cannot be labelled. The sample is communicated to the candidate node for labeling. If the candidate node is unsuccessful, a different candidate node may be identified to process and label the sample.