Neural Network Adaptation Modules for Efficient Model Tuning
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Solution Overview
Problem
Existing methods for adapting large-scale neural networks to new tasks are inefficient, particularly due to the need for extensive retraining and the addition of numerous parameters, which can lead to catastrophic forgetting and scalability issues.
Innovation Solution
The implementation of lightweight adaptation modules that learn to modify hidden responses within the neural network, using attention masks to guide the network towards relevant regions for the new task, without altering the original convolutional layers, allowing for efficient adaptation with minimal additional parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive retraining is performed to adapt neural networks to new tasks, then task performance is improved, but training time and computational resources increase significantly
Solution Approach 1:
The adaptation process is segmented into two distinct phases: (1) pre-training the base neural network on source domain data, and (2) applying lightweight adaptation modules on top of the frozen base network for target domain adaptation. This segmentation allows the majority of the network to retain pre-trained knowledge while only small adapter components require task-specific training, dramatically reducing training time while maintaining performance.
Solution Approach 2:
Instead of fully retraining the entire neural network for each new task, the invention applies partial action by only training the lightweight adaptation modules while keeping the base network frozen. This partial adaptation is sufficient to achieve good performance on new tasks without the excessive computational cost of complete retraining.
2Adaptability or versatility
If numerous parameters are added to adapt neural networks to new tasks, then adaptability is improved, but model complexity and risk of catastrophic forgetting increase
Solution Approach 1:
Lightweight adaptation modules serve as intermediary components between the base neural network and the task-specific requirements. These adapters act as mediators that enable the network to adapt to different tasks without directly modifying the base network parameters, thus maintaining simplicity while achieving high adaptability across multiple tasks.
Solution Approach 2:
The adaptation modules are nested within or attached to the existing neural network architecture, forming a hierarchical structure where small adapter components are embedded within the larger base network. This nesting allows the system to maintain the original network structure while adding adaptive capabilities with minimal additional parameters.
3Reliability
If the original convolutional layers are modified for adaptation, then task-specific performance is improved, but the risk of catastrophic forgetting increases
Solution Approach 1:
The base neural network is pre-trained on source domain data before being deployed for target tasks. This preliminary training establishes a solid foundation of general knowledge that is preserved by keeping the base network frozen during subsequent adaptation, preventing catastrophic forgetting while allowing task-specific adaptation through the lightweight modules.
Solution Approach 2:
Instead of modifying the original convolutional layers directly, the invention creates a copy of the base network and attaches adaptation modules to this copy. The original pre-trained network remains unchanged and intact, serving as a permanent reference that prevents loss of previously learned information while the adapted copy handles new tasks.
Data Source
AI summary
A neural network architecture for attention-based efficient model adaptation is disclosed. A method includes accessing an input vector, the input vector comprising a numeric representation of an input to a neural network. The method includes providing the input vector to the neural network comprising a plurality of ordered layers, wherein each layer in at least a subset of the plurality of ordered layers is coupled with an adaptation module, wherein the adaptation module receives a same input value as a coupled layer for the adaptation module, and wherein an output value of the adaptation module is pointwise multiplied with an output value of the coupled layer to generate a next layer input value. The method includes generating an output of the neural network based on an output of a last one of the plurality of ordered layers in the neural network.