Neural Network Document Classification with Open-Set Metric
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Solution Overview
Problem
Conventional document classification systems are limited to a small number of known document types and cannot effectively handle unknown labels, making them insufficient for real-world applications where hundreds of document types exist.
Innovation Solution
A document classification apparatus using a neural network that iteratively selects samples for annotation with an unknown class using an open-set metric, allowing for the identification of ambiguous samples and annotation with a previously unknown class, enabling a robust multi-label classifier to handle a larger number of labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional classification systems are used, then the system structure is simple and easy to implement, but the system is limited to a small number of known document types and cannot handle unknown labels
Solution Approach 1:
The patent implements dynamic label space expansion where the classification system evolves from a static set of predefined labels to a dynamic structure that can automatically incorporate unknown labels. The neural network iteratively selects samples for annotation with unknown classes based on open-set metric, allowing the label space to grow adaptively as new document types are encountered, thus resolving the contradiction between handling unknown labels and maintaining system simplicity.
Solution Approach 2:
The patent employs preliminary action through iterative sampling and annotation before final classification. The system pre-selects samples that are most likely to represent unknown classes based on their activation responses, annotates them with provisional labels, and then trains the neural network on these annotated samples. This preliminary processing enables the system to prepare for handling unknown labels in advance, expanding capability while managing complexity through structured preparation.
2Adaptability or versatility
If conventional training methods are used, then the training process is simple, but the method cannot annotate data without knowing the label space in advance
Solution Approach 1:
The patent implements feedback mechanisms where the neural network's predicted classification data is used to guide the selection of samples for annotation. The open-set metric calculates activation responses from the neural network's output, providing feedback about which samples are most uncertain or likely to represent unknown classes. This feedback loop enables the system to iteratively annotate unknown labels based on its own predictions, transforming the training process into an adaptive cycle that expands label space capability while managing training complexity through intelligent sample selection.
3Quantity of substance
If the number of known document types is increased, then the classification coverage improves, but the system becomes less able to handle unknown labels and reduces flexibility
Solution Approach 1:
The patent resolves this contradiction by making the label space dynamic rather than static. Instead of having a fixed number of known document types, the system maintains a neural network that can continuously identify and annotate new unknown classes. The iterative process of selecting samples based on open-set metric and annotating them with unknown classes allows the system to expand its known document types as needed, maintaining flexibility to handle both known and unknown labels simultaneously.
Solution Approach 2:
The patent uses preliminary action to prepare the system for handling unknown labels before they are encountered in production. By iteratively selecting and annotating samples that represent potential unknown classes during training, the system pre-expands its label space capability. This allows the system to maintain a large number of known document types while preserving the flexibility to accommodate new unknown labels, as the training process continuously prepares the model for such expansions.
Data Source
AI summary
Systems and methods for document classification are described. Embodiments of the present disclosure generate classification data for a plurality of samples using a neural network trained to identify a plurality of known classes; select a set of samples for annotation from the plurality of samples using an open-set metric based on the classification data, wherein the annotation includes an unknown class; and train the neural network to identify the unknown class based on the annotation of the set of samples.