Neural Network Parameter Search for Fast Task Adaptation
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Solution Overview
Problem
Conventional machine learning models, such as neural networks, require extensive retraining when transferred to different environments or tasks, necessitating a complete re-initialization from scratch, which is inefficient and resource-intensive.
Innovation Solution
The method employs evolvability search (Evolvability ES) to train neural networks by perturbing parameter vectors, maximizing diversity metrics like MaxVar or MaxEnt, allowing efficient adaptation to new tasks or environments with minimal retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional machine learning models are trained in a specific environment using a specific training data set, then the model achieves good performance in that environment, but the model cannot adapt to different environments or tasks and requires complete retraining from scratch
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on a source task and environment before deployment. The model is initially trained to perform well on a specific task using a specific training dataset, establishing a baseline performance. When transferred to a new environment or task, this pre-trained knowledge serves as a foundation that requires only minor adjustments rather than complete retraining, thereby reducing adaptation time while maintaining environmental specificity when needed
Solution Approach 2:
The patent employs parameter changes by modifying specific parameters of the pre-trained neural network when adapting to new environments or tasks. Instead of retraining all parameters from scratch, the system selectively adjusts certain parameters (such as adaptation parameters or subset of weights) to accommodate the new environment. This selective parameter modification enables the model to adapt quickly to different conditions while preserving the knowledge learned during pre-training, thus resolving the contradiction between adaptability and retraining time
2Adaptability or versatility
If conventional machine learning models are re-trained from scratch for new environments, then the model can perform well in the new environment, but the process is resource-intensive and inefficient
Solution Approach 1:
The patent applies the extraction principle by separating the training process into two distinct phases: an initial comprehensive pre-training phase on a source task, and a subsequent lightweight adaptation phase for new environments. The system extracts and preserves the generalizable knowledge from the source task training, then only adjusts specific parameters during adaptation. This extraction of essential knowledge from the complete retraining process reduces computational resource requirements while maintaining performance in new environments
Solution Approach 2:
The patent uses preliminary action by performing extensive training on a source task before deployment. This pre-training establishes a robust foundation of learned parameters and representations. When the model needs to adapt to a new environment, this preliminary training has already equipped the model with transferable knowledge, significantly reducing the computational resources needed for adaptation compared to training from scratch. The pre-trained model serves as an efficient starting point for environmental adaptation
3Adaptability or versatility
If neural networks are trained to be adaptable to multiple environments, then the model can transfer knowledge to new tasks, but the initial training and adaptation process becomes complex
Solution Approach 1:
The patent applies segmentation by dividing the training process into distinct phases: source task pre-training and target environment adaptation. This segmentation separates the complex tasks of learning general knowledge from adapting to specific environments. The pre-training phase focuses on acquiring transferable knowledge, while the adaptation phase handles environment-specific adjustments. This temporal and functional segmentation simplifies the overall training process complexity while enabling effective knowledge transfer across environments
Data Source
AI summary
Systems and methods are disclosed herein for training neural networks that can be adapted to new inputs, new tasks, new environment, etc. by re-training them efficiently. A parameter vector is initialized for a neural network. Perturbed parameter vectors are determined using the parameter vector. Behavior characteristics are determined for each perturbed parameter vector. The parameter vector is modified by moving it in the parameter vector space in a direction that maximizes a diversity metric. Other neural networks can be trained for new tasks or new environments using the parameter vector of the neural network.