Modular Deep Multitask Learning Framework for Cross-Domain Knowledge Sharing
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Solution Overview
Problem
Existing deep multitask learning approaches are limited by their monolithic nature, which restricts the discovery of generic functional modules that can be applied across diverse tasks, leading to inflexibility and inefficiency in solving complex problems.
Innovation Solution
A modular deep multitask learning framework that decomposes tasks into pseudo-tasks, aligns them across architectures, and shares learned parameters to preserve performance across diverse tasks, using hypermodules to generate block parameters and optimize alignment through a stochastic algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If monolithic feature extractors are used in deep multitask learning, then feature extraction capability is improved, but flexibility and adaptability across diverse tasks deteriorate
Solution Approach 1:
The patent segments the monolithic feature extractor into multiple independent functional modules that can be selectively combined. Each module performs a specific function (e.g., feature extraction, transformation, selection) and can be independently trained and reused across different tasks, thereby maintaining feature extraction capability while improving flexibility and adaptability.
2Adaptability or versatility
If generic functional modules are shared across diverse tasks, then adaptability and knowledge accumulation are improved, but device complexity increases
Solution Approach 1:
The patent designs functional modules with universal interfaces and standardized structures that allow them to be reused across multiple tasks and domains. Each module is self-contained with well-defined input/output interfaces, enabling them to function in different contexts without modification, thus improving adaptability while managing complexity through reuse.
Solution Approach 2:
The patent employs a hierarchical modular architecture where complex systems are built by nesting simpler modules within each other. This nested structure allows for organized complexity management, where high-level task-specific components can incorporate lower-level generic functional modules, thereby achieving adaptability without linearly increasing overall system complexity.
3Adaptability or versatility
If task decomposition into pseudo-tasks is implemented, then modularity and knowledge sharing are improved, but alignment complexity across architectures increases
Solution Approach 1:
The patent enforces homogeneous interfaces and standardized data formats across all pseudo-tasks and functional modules. This homogeneity in interface design, parameter structures, and data representations simplifies the alignment process by providing consistent conventions that reduce the complexity of integrating modules across different architectures and tasks.
Data Source
AI summary
A process for training and sharing generic functional modules across multiple diverse (architecture, task) pairs for solving multiple diverse problems is described. The process is based on decomposing the general multi-task learning problem into several fine-grained and equally-sized subproblems, or pseudo-tasks. Training a set of (architecture, task) pairs then corresponds to solving a set of related pseudo-tasks, whose relationships can be exploited by shared functional modules. An efficient search algorithm is introduced for optimizing the mapping between pseudo-tasks and the modules that solve them, while simultaneously training the modules themselves.