Graph-Based Temporal Classification for Ambiguous Label Training
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Solution Overview
Problem
Existing neural network training methods, such as Connectionist Temporal Classification (CTC), are inadequate for addressing label ambiguities and complexities beyond temporal alignment, particularly in situations where training labels are uncertain or partially labeled, leading to inefficiencies and increased computational resources.
Innovation Solution
Implement a Graph-Based Temporal Classification (GTC) objective function using a directed graph with non-monotonic alignment and constraints on label repetitions to resolve both temporal and label ambiguities, allowing for flexible rule imposition and efficient training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CTC objective function is used to resolve temporal ambiguity, then temporal alignment is improved, but label ambiguity and other types of ambiguities cannot be resolved
Solution Approach 1:
The patent segments the label sequence into multiple possible paths within a graph structure, where each path represents a different interpretation of ambiguous labels. This allows the system to handle both temporal alignment and label ambiguity by exploring multiple label sequences simultaneously rather than committing to a single sequence as in traditional CTC.
Solution Approach 2:
The patent adds a graph structure dimension to the traditional one-dimensional sequence processing. By representing labels as nodes in a graph with multiple possible paths, the system transforms the problem from simple sequence alignment to path finding in a multi-dimensional label space, enabling resolution of label ambiguities while maintaining temporal alignment.
2Measurement precision
If manual labeling is performed to ensure precise and accurate training labels, then label accuracy is improved, but the labeling process becomes laborious and time-consuming
Solution Approach 1:
The patent enables the system to self-correct and self-improve by using the graph-based structure to automatically explore and identify the most likely label sequences. The neural network processes ambiguous labels through multiple paths and automatically selects the optimal interpretation, eliminating the need for manual labeling while maintaining high accuracy.
Solution Approach 2:
The patent changes the parameter representation from fixed single labels to probabilistic multi-path label sequences. By representing labels as probability distributions over multiple possible paths in the graph, the system can handle uncertainty automatically without requiring manual disambiguation, thus reducing labeling time while preserving accuracy.
3Measurement precision
If CTC rules are applied to expand label sequences to match observation sequence length, then temporal alignment is achieved, but computational resources increase due to considering multiple expansion ways
Solution Approach 1:
The patent performs preliminary organization of label sequences into a graph structure before processing. By pre-defining valid label transitions and organizing possible label sequences into a graph with nodes and edges, the system eliminates the need to explore all possible expansions during training, significantly reducing computational resources while maintaining alignment precision.
Solution Approach 2:
The patent introduces dynamic path selection within the graph structure, where the system can adaptively choose which paths to explore based on the input data and current model state. This dynamic approach allows the system to focus computational resources on the most promising label sequences rather than uniformly processing all possible expansions, reducing overall computational burden.
4Loss of time
If automated labeling is used to reduce manual effort, then labeling time is reduced, but label accuracy becomes uncertain due to ambiguity
Solution Approach 1:
The patent implements feedback mechanisms where the graph-based model processes automated labels through multiple paths and uses the results to identify and correct ambiguities. The system continuously refines its label predictions by comparing different path interpretations and selecting the most consistent sequence, thereby maintaining high accuracy while using automated labeling processes.
Solution Approach 2:
The patent transforms fixed automated labels into probabilistic multi-path representations in the graph structure. By changing from deterministic single labels to probability distributions over multiple paths, the system can capture the uncertainty of automated labeling while still achieving accurate results through the collective information from multiple paths.
Data Source
AI summary
A method for training a neural network with a graph-based temporal classification (GTC) objective function, using a directed graph of nodes connected by edges representing labels and transitions among the labels, is provided. The directed graph specifies one or a combination of non-monotonic alignment between a sequence of labels and a sequence of probability distributions and constraints on the label repetitions. The method comprises executing a neural network to transform a sequence of observations into the sequence of probability distributions, and updating parameters of the neural network based on the GTC objective function configured to maximize a sum of conditional probabilities of all possible sequences of labels that are generated by unfolding the directed graph to the length of the sequence of observations and mapping each unfolded sequence of nodes and edges to a possible sequence of labels.