VAE Latent-Mean Retrieval for Faster Template Libraries
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Solution Overview
Problem
Existing template library construction methods, particularly those using discriminative neural networks, face inefficiencies in retrieval time and accuracy due to the use of complex evaluation indexes and large storage requirements, especially when dealing with large datasets.
Innovation Solution
Employ a generative variational autoencoder (VAE) to directly use the mean value of the latent vector as the template data representation, eliminating the need for distribution normalization and minimizing data differences, and utilize Euclidean distance for retrieval, reducing computational resources and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex evaluation indexes are used in template library construction, then retrieval accuracy may improve, but retrieval time and computational load increase
Solution Approach 1:
The patent extracts only the essential mean value from the latent vector distribution, discarding the complexity of full distribution normalization and complex evaluation indexes. This extraction of the core feature (mean value) maintains retrieval accuracy while significantly reducing computational load and retrieval time.
Solution Approach 2:
The patent changes the parameter representation from complex distribution-based evaluation indexes to simple mean value with Euclidean distance. This parameter transformation simplifies the retrieval process while maintaining effectiveness, directly resolving the contradiction between accuracy and speed.
2Manufacturing precision
If distribution normalization and minimizing data differences are performed, then template representation accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The patent extracts only the mean value from the latent vector distribution, eliminating the need for full distribution normalization. This extraction maintains template representation accuracy by capturing the essential feature while dramatically reducing computational resource requirements.
Solution Approach 2:
The patent uses a simplified mean value representation instead of complex normalized distributions, effectively replacing expensive computational processes with a cheaper, more efficient approximation that achieves the same practical purpose.
3Productivity
If generative variational autoencoder is used to directly store mean value as latent vector, then template library construction efficiency improves, but randomness in generative models may affect accuracy
Solution Approach 1:
The patent extracts only the deterministic mean value from the probabilistic latent vector distribution, eliminating randomness while preserving the essential template information. This extraction ensures reliability by using a stable, reproducible representation.
Solution Approach 2:
Instead of using the full probabilistic distribution as traditionally done in VAEs, the patent inverts the approach by using only the mean value, transforming a probabilistic problem into a deterministic one that maintains accuracy while improving efficiency.
Data Source
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AI summary
The present application relates to a template library construction and retrieval method and apparatus. The method comprises: a template library construction step, involving: for template images, on the basis of a variational autoencoder, acquiring hidden vectors corresponding to the template images, and storing the hidden vectors to construct a template library; and a template library retrieval step, involving: for a test image, on the basis of a variational autoencoder, acquiring a hidden vector corresponding to the test image, and using the hidden vector to perform retrieval in the template library, so as to obtain template data. In the present application, by means of such a template library construction and retrieval method and apparatus, the template library construction and retrieval efficiency can be improved.