Machine Learning Model Grafting via Bridge Translation
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Solution Overview
Problem
Machine learning systems face inefficiencies when multiple models are required, as each model processes input data using its own backbone and head, leading to wasteful use of processing and memory resources, particularly in resource-constrained devices, and preventing deployment on such devices.
Innovation Solution
The technique involves selecting one machine learning model to retain its backbone, using inception to learn how the non-selected models' backbones respond to the environment, and training a bridge to translate features from the selected model's backbone into those usable by the non-selected models' heads, allowing the non-selected models' backbones to be discarded, thereby reducing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple machine learning models each use their own backbone and head, then each model can perform its specific task independently, but processing and memory resources are wasted
Solution Approach 1:
The machine learning model is segmented into two independent components: a shared backbone and separate heads. The backbone processes input data to generate features that are then distributed to multiple heads, each responsible for a specific task. This segmentation allows resource sharing while maintaining task-specific performance.
Solution Approach 2:
The backbone serves as a universal feature extractor that can support multiple different heads and tasks simultaneously. By making the backbone multi-functional, the system avoids duplicating processing capabilities across multiple models, thereby reducing overall resource consumption while maintaining the ability to perform various machine learning tasks.
2Adaptability or versatility
If multiple machine learning models are deployed on resource-constrained devices, then more functionality is available, but the devices lack sufficient processing and memory resources
Solution Approach 1:
By segmenting models into shared backbones and separate heads, the system reduces the overall computational burden on resource-constrained devices. The shared backbone is executed once to generate features, which are then processed by multiple lightweight heads, distributing functionality without duplicating heavy processing components.
Solution Approach 2:
Multiple machine learning models are merged into a unified architecture where they share a common backbone. This consolidation reduces device complexity by eliminating redundant processing components while maintaining the functionality of multiple models through shared feature representations and task-specific heads.
3Use of energy by moving object
If a shared backbone is used for multiple models, then resource usage is reduced, but the backbone must be trained to generate features usable by multiple different heads
Solution Approach 1:
The backbone is trained in advance using a combination of task-specific training data and synthesized data generated by the heads. This preliminary training ensures the backbone learns to generate features that are useful across multiple tasks before deployment, simplifying the overall training process and reducing the need for complex iterative training of multiple separate models.
Solution Approach 2:
Synthesized data acts as an intermediary during the training process, bridging the gap between the backbone and multiple heads. The synthesized data, generated by the heads from the backbone's features, provides feedback signals that guide the backbone's learning to produce universally useful features without requiring direct access to all task-specific training data.
Data Source
AI summary
A method includes obtaining multiple machine learning models, where each machine learning model includes a backbone and a head. The method also includes selecting a first of the machine learning models to retain its backbone. The method further includes back-propagating error terms for synthetic activation data through at least a portion of the backbone of a second of the machine learning models to generate an inception basis set. In addition, the method includes configuring a bridge using the inception basis set, where the bridge is configured to translate features generated by the backbone of the first machine learning model into features for use by the head of the second machine learning model.