Unified Encoder Decoding Shared Features
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Solution Overview
Problem
Conventional machine-learning algorithms are designed for single tasks and incur redundancy when performing multiple tasks, wasting computational resources and not leveraging deep neural networks for generic representations, especially in devices with limited bandwidth and onboard autonomy.
Innovation Solution
A scalable and distributed machine learning framework with a unified encoder-decoder architecture that uses mutual transfer learning and modular neural networks to perform multiple tasks efficiently, sharing common components and features across tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple independently trained models are used to perform different tasks, then each task can be executed with dedicated optimization, but computational resources are wasted due to redundancy and the system complexity increases
Solution Approach 1:
The patent merges multiple independently trained models into a unified encoder-decoder framework where a single encoder shares its learned representations across multiple task-specific decoders. This combining approach eliminates redundant encoding operations while maintaining task-specific optimization through dedicated decoders, thus reducing system complexity while preserving execution accuracy.
Solution Approach 2:
The unified encoder is designed to serve multiple functions by generating shared latent representations that can be utilized by different decoders for various tasks. This multi-functional encoder reduces the overall system complexity by replacing multiple specialized encoders with a single universal component that benefits all tasks through shared learning.
2Reliability
If multiple independently trained models are executed simultaneously, then each task receives dedicated computational resources, but computational efficiency decreases due to redundancy
Solution Approach 1:
The patent combines multiple encoding operations into a single unified encoder that generates shared latent representations. This merging eliminates redundant computational work while maintaining task-specific performance through specialized decoders, thereby improving computational efficiency without sacrificing task performance.
Solution Approach 2:
The framework discards redundant encoding operations by using a shared encoder for multiple tasks, and recovers task-specific performance information through dedicated decoders that specialize in their respective tasks. This approach improves computational efficiency by eliminating waste while preserving necessary task-specific capabilities.
3Ease of manufacture
If conventional single-task algorithms are used, then implementation is simpler, but computational resources are wasted and scalability to multiple tasks is poor
Solution Approach 1:
The unified encoder is designed as a universal component that can serve multiple tasks through shared latent representations. This multi-functional design maintains implementation simplicity by using a single encoder architecture while dramatically improving adaptability to multiple tasks through the addition of task-specific decoders.
Solution Approach 2:
The system segments functionality into a shared encoder component and task-specific decoder components. This segmentation maintains implementation simplicity for the core encoding operation while enabling easy adaptation to new tasks by adding or modifying decoders without changing the encoder architecture.
4Reliability
If task-specific models are trained independently, then each model can be optimized for its specific task, but shared information across tasks is not exploited
Solution Approach 1:
The patent merges the training process of multiple tasks through a shared encoder that learns from all tasks simultaneously. This combined training approach allows the encoder to capture shared information and patterns across tasks, improving knowledge transfer while maintaining task-specific optimization through dedicated decoders.
Solution Approach 2:
The unified encoder serves as a universal learning component that extracts shared representations beneficial to multiple tasks. This multi-functional encoder improves adaptability by learning common patterns across tasks while task-specific decoders maintain optimization for individual tasks through their specialized architectures.
Data Source
AI summary
A computer implemented system for interpreting data using machine learning, including one or more processors; one or more memories; and one or more computer executable instructions embedded on the one or more memories, wherein the computer executable instructions are configured to execute a unified encoder comprising a neural network encoding data into one or more feature vectors, wherein the encoder is trained using machine learning to generate the one or more feature vectors useful for performing a plurality of different tasks each comprising different interpretations of the data. A plurality of decoders are connected to the unified encoder, each of the decoders comprising a neural network interpreting the one or more feature vectors so as to decode one or more of the feature vectors to output one of the interpretations.