Automated Training Data Generation for Deep Learning Models
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Solution Overview
Problem
Junior developers face significant challenges in efficiently generating training data for deep learning models, requiring extensive knowledge and time due to the need for manual data processing, model selection, and parameter tuning during the fine-tuning process.
Innovation Solution
A method and apparatus for automatically generating training data based on the type of original data provided, which includes categorical data with labels, session data with labels, and unlabeled data, using techniques such as data enhancement and grouping samples into positive and negative categories, reducing the user's workload and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If developers manually process task data and select loss functions during fine-tuning, then model training can be performed, but the workload and time required increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically detecting data types and generating appropriate training data processing schemes before the actual fine-tuning process begins. This includes automatically identifying whether input data is categorical or session data, selecting appropriate loss functions, and preparing training datasets in advance, thereby reducing the time required during the actual model training phase.
Solution Approach 2:
The system enables self-service by allowing developers to simply input original data and desired model parameters, while the system automatically handles data processing, type detection, loss function selection, and training data generation. This eliminates the need for developers to manually process task data and select hyperparameters, significantly improving productivity.
2Ease of operation
If developers need to process task data according to actual situation and select loss functions based on experience, then model customization is achieved, but the complexity of operation increases
Solution Approach 1:
The system performs self-service by automatically detecting data types and selecting appropriate processing schemes without requiring developer intervention. The system autonomously identifies whether data is categorical or session data, selects suitable loss functions based on detected types, and generates training datasets automatically, thereby simplifying operations while maintaining customization capabilities.
Solution Approach 2:
The system manages complexity by dynamically changing processing parameters based on detected data types. When categorical data is detected, it applies one set of processing parameters; when session data is detected, it switches to a different set of parameters including different loss function selections. This automatic parameter adaptation simplifies the user interface while handling complex processing logic internally.
3Adaptability or versatility
If pre-trained models are released to the network for sharing, then other developers can use them for fine-tuning, but the need for manual data processing and model selection remains
Solution Approach 1:
The system enables self-service by automatically handling the entire fine-tuning preparation process. When developers use pre-trained models, the system automatically detects the type of task data provided, generates appropriate training datasets, selects suitable loss functions, and configures training parameters, thereby maintaining model adaptability while significantly improving ease of operation.
Solution Approach 2:
The system achieves universality by designing a unified processing framework that handles multiple data types (categorical and session data) and multiple model fine-tuning scenarios through a single automated interface. This multi-functional system can process various types of original data and generate appropriate training schemes for different deep learning models, making the fine-tuning process accessible to developers with varying levels of expertise.
Data Source
AI summary
A method and an apparatus for generating training data are provided. The training data is used for training a target deep learning model. In the method, original data for generating the target deep learning model is obtained from a user. Then, a type of the original data is determined. The type of the original data includes categorical data with label, session data with label, and data without label. A label of the categorical data indicates a category of the categorical data. A label of the session data indicates a question-answer relevance of the session data. Next, the training data is generated according to the type of the original data.