Neural Network Task Segmentation to Prevent Catastrophic Forgetting
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Solution Overview
Problem
Existing neural networks face challenges in efficiently performing multiple machine learning tasks without suffering from catastrophic forgetting, negative transfer, gradient interference, and security breaches, while maintaining computational efficiency and privacy.
Innovation Solution
A neural network system is trained to perform multiple tasks by leveraging existing parameters, using parallelization and selective updates to new networks, freezing shared blocks, and designating secure networks to maintain performance and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a neural network is trained for a new task by updating existing parameters, then the network can learn the new task, but it suffers from catastrophic forgetting of previous tasks
Solution Approach 1:
The neural network is divided into multiple independent networks, each trained to perform a specific task. Each network maintains its own parameters and is trained separately on its designated task, allowing the system to handle multiple tasks simultaneously without catastrophic forgetting, as updating one network does not affect the others.
Solution Approach 2:
The system creates a universal neural network system that can perform multiple different machine learning tasks through a collection of specialized networks. Each network within the system is optimized for a specific task, and the system as a whole provides multi-functionality by selecting and deploying the appropriate network for each task.
2Reliability
If multiple neural networks are trained to perform different tasks, then task performance improves, but computational cost and training time increase
Solution Approach 1:
The system extracts and trains multiple candidate networks in parallel, each targeting a specific task. By training candidates separately and independently, the computational workload is distributed across parallel processes rather than sequentially updating a single network for multiple tasks, reducing overall training time and computational cost.
Solution Approach 2:
Instead of training one network to perfection on multiple tasks, the system trains multiple networks partially on their respective tasks. Each network is specialized for a specific task rather than being general-purpose, allowing for more efficient training with less computational resource expenditure per network while achieving better overall task performance.
3Adaptability or versatility
If new network parameters are added to perform new tasks, then the network gains new capabilities, but latency and computational cost increase
Solution Approach 1:
The system segments the neural network functionality into separate specialized networks. When a new task is needed, a new candidate network is trained and added to the system rather than expanding an existing network. This allows the system to gain new capabilities while keeping each individual network compact and efficient for its specific task.
Solution Approach 2:
The system creates copies of neural network architectures tailored for specific tasks. Each candidate network is a copy of the base architecture but trained and configured for a particular task. This copying approach allows rapid deployment of new capabilities without modifying existing networks, maintaining their original performance characteristics and latency.
4Device complexity
If neural networks share parameters to reduce complexity, then device complexity decreases, but gradient interference occurs during training
Solution Approach 1:
The system segments the parameter spaces of different networks, giving each candidate network its own independent parameters. This segmentation eliminates gradient interference between tasks during training, as each network's parameters are updated independently based on its own task's gradients without conflicting with other tasks' parameter updates.
Solution Approach 2:
The system dynamically manages the set of active neural networks based on task requirements. Networks are added or removed from the active set depending on which tasks need to be performed, allowing the system to adapt its complexity dynamically rather than maintaining a fixed complex structure for all possible tasks.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network system to perform multiple machine learning tasks.