Latent Space Encoding for Multi-Class Image Robustness
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Solution Overview
Problem
Existing encoding and decoding systems from latent spaces lack the ability to robustly encode and decode different and irrelevant images simultaneously, failing to effectively map high-dimensional data to a common latent space that represents various input classes.
Innovation Solution
The system maps high-dimensional modalities of data from latent probability distributions to multiple independent latent spaces and then combines these spaces into a common latent space using multi-stage latent learning, enabling encoders to learn characteristics of different images and decode them accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing encoding and decoding systems map data to latent spaces, then encoding and decoding can be performed, but the systems fail to robustly encode and decode different and irrelevant images simultaneously
Solution Approach 1:
The patent divides the latent space into multiple independent latent spaces, each associated with a specific encoder-decoder pair. This segmentation allows each latent space to be optimized for specific image characteristics while maintaining the ability to handle multiple image classes simultaneously through the common latent space architecture.
Solution Approach 2:
The patent creates a universal common latent space that serves multiple functions: it represents features from different image classes, enables robust encoding and decoding across diverse inputs, and facilitates transfer learning between different image datasets. The common latent space acts as a multi-functional hub that integrates information from multiple independent latent spaces.
2Device complexity
If high-dimensional data is mapped to latent spaces, then dimensionality reduction is achieved, but the ability to represent various input classes is compromised
Solution Approach 1:
The patent transitions from a single high-dimensional latent space to multiple lower-dimensional independent latent spaces, each capturing specific features. These are then integrated into a common latent space that maintains comprehensive representation of input classes. This dimensional transformation preserves information while reducing complexity.
Solution Approach 2:
The patent embeds multiple independent latent spaces within the common latent space structure. Each independent latent space is nested within the overall common latent space framework, allowing hierarchical organization of features from different image classes while maintaining a unified representation structure.
3Manufacturing precision
If multiple encoder-decoder pairs are used for different image classes, then specialized encoding can be achieved, but system complexity increases
Solution Approach 1:
The patent merges multiple independent latent spaces into a unified common latent space while preserving the specialized encoding capabilities of individual encoder-decoder pairs. This combining approach allows the system to maintain precision for specific image classes through individual encoders while sharing computational resources and representation space through the common latent space, thereby reducing overall system complexity.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to a process to facilitate learning a model for clustered encoding and decoding from a latent probability distribution. A computer implemented method can comprise mapping, by a system operatively coupled to a processor, high-dimensional modalities of data from one or more latent probability distributions corresponding to a plurality of encoder and decoder pairs to a plurality of independent latent spaces. The computer implement method can also comprise mapping, by the system, the plurality of independent latent spaces to a common latent space representing one or more features of one or more input classes associated with the plurality of encoder and decoder pairs.