Encoder-Decoder Content Generation for Consistent Content Metadata
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Solution Overview
Problem
Existing methods for generating content information independently from content lead to errors between the two, necessitating a technology that allows simultaneous and interactive generation within a single network.
Innovation Solution
A content generation method using a machine learning model with an encoder and decoder to generate feature vectors, enabling simultaneous and interactive production of content and content information within a single network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content and content information are generated independently using separate methods, then the generation process is simpler and faster, but errors occur between the content and content information leading to inconsistency
Solution Approach 1:
The patent merges the content generation process and content information generation process into a single unified neural network model. The encoder processes input content to extract features, and the decoder simultaneously generates both the transformed content and associated content information (such as captions, labels, or annotations) in an integrated manner, ensuring consistency between them.
Solution Approach 2:
The unified neural network model performs multiple functions: it processes input content, extracts meaningful features through the encoder, generates transformed content through the decoder, and simultaneously produces accurate content information. This multi-functional approach eliminates the need for separate independent generation systems.
2Measurement precision
If a unified neural network is used to generate content and content information simultaneously, then consistency and accuracy are improved, but the model complexity and training difficulty increase
Solution Approach 1:
The unified model is segmented into distinct functional components: an encoder that processes input content and extracts features, and a decoder that generates both transformed content and content information. This segmentation manages complexity by organizing the model into manageable, functionally distinct modules while maintaining their integrated operation for consistent output.
Solution Approach 2:
The encoder acts as an intermediary that processes input content and produces feature representations that are then used by the decoder to generate both transformed content and content information. This intermediary structure facilitates the complex task of simultaneous generation by providing a shared feature space that ensures consistency between outputs.
Data Source
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AI summary
A content generation method includes acquiring at least one first content, and generating, using a machine learning model, at least one second content associated with the at least one first content. The machine learning model includes an encoder configured to generate at least one feature vector based on the at least one first content, and a decoder configured to generate the at least one second content based on the generated at least one feature vector.