Multi-Task Neural Network Structural Learning
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Solution Overview
Problem
Existing multi-task learning in artificial neural networks faces challenges such as task interference and inefficient computational costs due to static architectures, which hinder effective scene understanding and information transfer between tasks.
Innovation Solution
A computer-implemented method for multi-task structural learning that utilizes neuron creation and removal based on local task similarity, employing Centered Kernel Alignment to align task node features and create group nodes through a weighted sum of individual task losses, and a regularization term to maximize alignment between task nodes, transitioning from a dense to a sparse architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multi-task learning with shared layers is used, then inference efficiency is improved and information transfer between tasks is enhanced, but task interference occurs
Solution Approach 1:
The patent applies dynamics by making the network architecture adaptive rather than static. The system dynamically creates and removes neurons based on task similarity metrics during training, allowing the architecture to evolve and optimize itself for the specific multi-task scenario, thereby reducing task interference while maintaining efficiency
Solution Approach 2:
The patent changes parameters by introducing a similarity threshold parameter that controls neuron creation and removal. By adjusting this parameter and using similarity metrics to determine architectural modifications, the system optimizes the balance between task sharing and task interference mitigation
2Ease of manufacture
If static architecture is used in multi-task learning, then implementation is simpler, but computational efficiency is reduced and task interference increases
Solution Approach 1:
The system transitions from static to dynamic architecture by implementing automatic neuron creation and removal based on task similarity. This dynamic adaptation improves computational efficiency by optimizing the network structure for each multi-task scenario without requiring manual architectural design
Solution Approach 2:
The network performs self-optimization by automatically creating or removing neurons based on computed task similarity metrics. This self-service mechanism eliminates the need for manual architectural intervention while achieving optimal computational efficiency for the given tasks
3Loss of information
If dense architecture is used, then information transfer between tasks is enhanced, but task interference increases and computational cost rises
Solution Approach 1:
The patent applies local quality by creating task-specific neurons only where needed based on local task similarity patterns. Rather than uniformly sharing all neurons across tasks, the system selectively creates shared neurons in specific locations where tasks are similar, thereby maintaining information transfer while reducing interference
Solution Approach 2:
The architecture becomes porous by selectively removing neurons that cause task interference while maintaining connections where tasks are similar. This creates an optimized information flow path that allows necessary information transfer while filtering out interfering signals between dissimilar tasks
Data Source
AI summary
A computer-implemented method for multi-task structural learning in artificial neural network in which both the architecture and its parameters are learned simultaneously. The method utilizes two neural operators, namely, neuron creation and neuron removal, to aid in structural learning. The method creates excess neurons by starting from a disparate network for each task. Through the progress of training, corresponding task neurons in a layer pave the way for a specialized group neuron leading to a structural change. In the task learning phase of training, different neurons specialize in different tasks. In the interleaved structural learning phase, locally similar task neurons, before being removed, transfer their knowledge to a newly created group neuron. The training is completed with a final fine-tuning phase where only the multi-task loss is used.