Two-Phase Object Processing with Pre-Trained Generic Models
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Solution Overview
Problem
The inefficiency and high cost of training multiple machine learning models separately for different tasks, along with poor generalization ability and rapid performance decay, are significant challenges in multi-task scenarios.
Innovation Solution
A two-phase model architecture is employed, using a pre-trained generic model for initial feature extraction followed by individual model training based on generic features and annotation information, enhancing model training efficiency and generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple machine learning models are trained separately for different tasks, then each model can be optimized for its specific task, but the training time and computational resources increase significantly
Solution Approach 1:
The model training process is segmented into two distinct phases: a pre-training phase where a generic model learns from multi-task data, and a fine-tuning phase where individual models are trained for specific tasks. This segmentation allows the system to reuse the pre-trained generic model across multiple tasks, significantly reducing the total training time while maintaining task-specific performance optimization.
Solution Approach 2:
The pre-training phase performs preliminary action by training a generic model on diverse multi-task data before the specific task models are needed. This preliminary training establishes a strong foundation that can be quickly adapted to specific tasks through fine-tuning, reducing the time required for each individual model training while ensuring task-specific optimization.
2Measurement precision
If separate machine learning models are trained for each task, then each model can achieve high accuracy for its specific task, but the computational resources and data annotation requirements increase
Solution Approach 1:
A single generic model is trained to perform multiple tasks simultaneously during the pre-training phase, making it universal across different task types. This multi-functionality reduces the need for separate data annotation for each task, as the generic model learns shared patterns from diverse data. Individual models then leverage this pre-trained knowledge to achieve high accuracy with less task-specific annotated data.
3Device complexity
If fixed machine learning models are used, then the system structure is simple, but the models cannot meet processing requirements under different scenarios
Solution Approach 1:
The system transitions from static fixed models to a dynamic two-phase architecture where a generic model can be adaptively fine-tuned for different tasks. The system dynamically selects and adapts the appropriate model configuration based on the specific task requirements, enabling versatility while maintaining a relatively simple overall structure through code reuse and parameter sharing.
Data Source
AI summary
Embodiments of the disclosure provide a method, an apparatus, a device and a storage medium for object processing. The method for object processing includes: in response to receiving a predetermined operation by a user on a first selection control for pre-trained at least one generic model presented in a user interface, selecting the at least one generic model; at least acquiring at least one generic feature that is generated by the at least one generic model and associated with a sample of an object of a target category among a plurality of categories; and training an individual model for processing the object of the target category at least based at least on the at least one generic feature and annotation information of the sample of the object of the target category. Therefore, the efficiency of model training can be improved.