Multi-Task Neural Network Training With Frozen Shared Blocks
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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 issues, while maintaining performance and efficiency.
Innovation Solution
A system trains a neural network system to perform multiple tasks by leveraging existing parameters, parallelizing training operations, and freezing shared network blocks to preserve knowledge, ensuring secure information storage and minimizing computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a neural network is trained for multiple tasks using existing parameters, then the number of additional network parameters required decreases, but the risk of catastrophic forgetting increases
Solution Approach 1:
The neural network is divided into shared network blocks that are common across multiple tasks and task-specific network blocks that are unique to each task. The shared blocks are frozen during training of new tasks, while only the task-specific blocks are updated. This segmentation allows the network to learn new tasks without interfering with previously learned tasks, resolving the contradiction between parameter efficiency and preventing catastrophic forgetting.
Solution Approach 2:
The shared network blocks are pre-trained and frozen before training new tasks. This preliminary action preserves the knowledge encoded in these blocks, ensuring that training new tasks does not degrade performance on previous tasks. The frozen shared blocks serve as a stable foundation that prevents catastrophic forgetting while allowing new learning to occur in the task-specific blocks.
2Productivity
If training operations are parallelized across multiple threads or cores, then training time and computational cost decrease, but system complexity increases
Solution Approach 1:
The training operations are segmented into independent tasks, each handled by a separate neural network trained in parallel. Each neural network processes a specific task independently using its own task-specific network blocks, while sharing the same frozen shared network blocks. This segmentation enables parallel training without complex coordination, improving training speed while keeping the architecture manageable.
Solution Approach 2:
The shared network blocks serve multiple functions across different tasks, acting as a universal component that is common to all neural networks in the system. This multi-functionality allows parallel training of multiple tasks without requiring separate dedicated resources for each task, reducing overall system complexity while maintaining high productivity.
3Adaptability or versatility
If new network parameters are added to encode new information, then the neural network's capability for new tasks improves, but latency and computational cost at inference time increase
Solution Approach 1:
The network is segmented into shared blocks that are common to all tasks and task-specific blocks that are only activated for their respective tasks. At inference time, only the task-specific blocks are activated along with the shared blocks, minimizing the number of parameters processed. This segmentation ensures that adding new tasks does not increase inference latency for existing tasks, as the frozen shared blocks remain inactive for new task inference.
Solution Approach 2:
Instead of training all network parameters for every possible task, the system uses a partial action approach where only the task-specific network blocks are trained and activated for each specific task. The shared blocks are frozen and not updated for new tasks. This partial training approach reduces the effective number of parameters that need to be processed at inference time, maintaining speed while enabling versatility.
4Adaptability or versatility
If existing neural networks are updated for new tasks, then the network can perform new functions, but catastrophic forgetting occurs and performance on previous tasks degrades
Solution Approach 1:
The network architecture is segmented into shared network blocks that are common across all tasks and task-specific network blocks that are unique to each task. When training a new task, only the task-specific blocks are updated while the shared blocks remain frozen. This segmentation ensures that updates to new tasks do not affect previously learned tasks, enabling the network to perform new functions without forgetting previous ones.
Solution Approach 2:
The shared network blocks are pre-trained and frozen before training new tasks. This preliminary freezing action preserves the knowledge encoded in these blocks, preventing catastrophic forgetting. The frozen shared blocks serve as a stable foundation that maintains performance on previous tasks while allowing new learning to occur in the task-specific blocks.
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.