Neural Network Training via Reinforcement Learning Hyperparameter Tuning
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Solution Overview
Problem
Training neural networks for image processing tasks is time-consuming and resource-intensive, often requiring manual intervention due to challenges in synthetic-to-real generalization and the need for manual tuning of hyperparameters.
Innovation Solution
A framework that incorporates a reinforcement learning and learning-to-optimize (RL-L2O) approach to automate the adjustment of learning rates and enforce representational similarity between synthetically trained and pre-trained models, using synthetic-to-real transfer learning with proxy guidance to improve generalization and reduce manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tuning of hyperparameters is used to train neural networks, then training precision can be improved, but training time and resource consumption increase significantly
Solution Approach 1:
The system employs reinforcement learning agents that automatically tune hyperparameters without human intervention. The agents learn optimal hyperparameter configurations through self-directed exploration and exploitation, enabling the training process to self-optimize while reducing manual tuning requirements and training time.
Solution Approach 2:
The reinforcement learning framework implements continuous feedback loops where training performance metrics are fed back to the agents, which then adjust hyperparameters accordingly. This closed-loop control enables automatic adaptation to finding optimal training configurations, improving precision while reducing the time needed for manual intervention.
2Productivity
If synthetic data is used for pre-training neural networks, then training efficiency is improved, but generalization to real-world data deteriorates
Solution Approach 1:
The system performs preliminary pre-training on synthetic data to establish initial weight configurations and learn basic patterns efficiently. This preliminary action accelerates the early stages of training, after which the model is fine-tuned on real-world data to ensure proper generalization, combining the benefits of both approaches.
Solution Approach 2:
The reinforcement learning agents operate in a hyperparameter optimization space, adding an additional dimension to the training process. By optimizing learning rates, batch sizes, and other hyperparameters specifically for synthetic-to-real transfer scenarios, the system bridges the gap between synthetic pre-training efficiency and real-world generalization reliability.
3Extent of automation
If reinforcement learning is used to automate hyperparameter tuning, then manual intervention is reduced, but system complexity increases
Solution Approach 1:
The reinforcement learning framework is designed to handle multiple hyperparameter tuning tasks across different neural network architectures and training scenarios through a unified system. The same RL agents can optimize various hyperparameters (learning rates, batch sizes, momentum values) for different models, reducing the need for separate manual tuning processes and justifying the initial complexity through broad applicability.
Data Source
AI summary
Apparatuses, systems, and techniques to train one or more neural networks. In at least one embodiment, one or more neural networks are trained based, at least in part, on inferencing output from one or more second neural networks.