Adaptive Classification Model With Early-Exit Probability Thresholds
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing classification models use a uniform network structure regardless of data difficulty, leading to prolonged prediction times and suboptimal accuracy within a limited timeframe.
Innovation Solution
A method for training a classification model that involves setting probability thresholds for each convolutional layer, allowing for early prediction termination based on these thresholds, thereby optimizing the model's efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a uniform network structure is used for all data regardless of difficulty, then the model structure is simple, but the prediction time becomes too long and maximum correct rate cannot be obtained in limited time
Solution Approach 1:
The patent applies dynamics by making the network structure adaptive rather than static. The model dynamically adjusts the number of convolutional layers to process based on the difficulty of input data, transitioning from a fixed uniform structure to a flexible dynamic structure that optimizes both accuracy and speed according to data characteristics
Solution Approach 2:
The patent applies local quality by treating different data differently through selective application of network layers. Instead of applying the same uniform structure to all data, the model applies varying depths of convolutional layers tailored to the specific difficulty level of each data sample, optimizing local processing quality
2Device complexity
If a uniform network structure is used for all data regardless of difficulty, then the model structure is simple, but the maximum correct rate cannot be obtained in limited time
Solution Approach 1:
The model dynamically adjusts the number of convolutional layers based on data difficulty to maximize classification accuracy within time constraints. By making the network depth adaptive rather than fixed, the system can allocate more computational resources to difficult data when time permits, thereby maximizing correct rate without requiring a uniformly complex structure for all inputs
Solution Approach 2:
The patent changes the parameter of network depth (number of convolutional layers) based on data characteristics. By varying this key parameter according to data difficulty and time constraints, the model achieves optimal accuracy for each specific input without being forced to use a uniformly deep structure that would waste time on easy data
3Reliability
If the classification process continues through all layers, then the classification accuracy is maximized, but the prediction time becomes too long
Solution Approach 1:
The patent applies partial action by stopping the classification process at an optimal point rather than always processing through all layers. The model determines when sufficient accuracy has been achieved and terminates processing early, avoiding excessive computation on data that doesn't require full-depth processing, thus reducing time loss while maintaining adequate accuracy
Solution Approach 2:
The model incorporates feedback mechanisms to monitor classification confidence and accuracy at each layer. Based on this feedback, the system determines when to stop processing - when the current layer's output achieves sufficient accuracy or when time constraints are approached - allowing dynamic termination that balances accuracy and speed
Data Source
AI summary
A method and an apparatus for training a classification model and data classification includes: obtaining a sample set and a pre-trained classification model, wherein the classification model includes at least two convolutional layers, each convolutional layer is connected to a classification layer through a fully connected layer; inputting the sample set into the classification model, and obtaining a prediction result output by each classification layer, wherein the prediction result includes a prediction probability of a class to which each sample belongs; calculating a probability threshold of each classification layer based on the prediction result output by each classification layer; setting a prediction stopping condition for the classification mode according to the probability threshold of each classification layer.