Autoencoder Training Method for Probability Distribution Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing autoencoders face challenges in matching the probability distribution of real data in the real space with that of feature data in the latent space, leading to difficulties in improving data analysis accuracy, particularly in anomaly detection.
Innovation Solution
A training method for autoencoders that involves encoding input data, calculating the probability distribution of feature data, adding noise to the feature data, decoding it, and minimizing the error and information entropy to ensure the probability distributions of both the input and feature data are proportional.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional unsupervised learning is used to learn latent variables, then the autoencoder can extract feature data from real data, but the probability distribution of real data in real space cannot be effectively matched with the probability distribution of feature data in latent space
Solution Approach 1:
The patent changes the training objective parameters from traditional reconstruction error alone to a combined objective function that includes both reconstruction error and probability distribution matching terms. Specifically, it minimizes the difference between the probability distribution of real data and the probability distribution of decoded feature data, thereby aligning the statistical properties of both spaces while maintaining dimensionality reduction capabilities.
2Manufacturing precision
If the autoencoder focuses on minimizing reconstruction error, then decoded data closely matches input data, but the probability distribution matching between real space and latent space deteriorates
Solution Approach 1:
The patent merges two previously separate objectives into a unified training framework: (1) reconstruction accuracy optimization and (2) probability distribution matching. The combined loss function simultaneously minimizes both the reconstruction error and the distributional difference, ensuring that the autoencoder learns features that are both faithful to the original data and statistically consistent across spaces.
Solution Approach 2:
The patent introduces a feedback mechanism where the probability distribution matching performance is continuously monitored during training and used to adjust the learning process. By computing the difference between real data distribution and decoded feature data distribution, the system provides feedback signals that guide the optimization of encoding and decoding parameters to achieve better distribution alignment.
Data Source
AI summary
A training method of an autoencoder that performs encoding and decoding, for a computer to execute a process includes encoding input data by the autoencoder; obtaining a probability distribution of feature data obtained by encoding the input data by the autoencoder; adding a noise to the feature data; generating decoded data by decoding the feature data to which the noise is added by the autoencoder; and training the autoencoder to train the probability distribution of the feature data so that an information entropy of the probability distribution and an error between the decoded data and the input data are decreased.