Gradient Normalization for Adaptive Loss Balancing in Multitask Networks
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Solution Overview
Problem
Deep multitask networks face challenges in balancing training across multiple tasks due to imbalances in gradient magnitudes, leading to suboptimal training dynamics and overfitting, especially when tasks have different loss scales and complexities.
Innovation Solution
The GradNorm method dynamically adjusts gradient norms to balance training rates by normalizing gradients across tasks using a hyperparameter α, which adjusts the relative training rate for each task, ensuring that tasks train at similar rates and reducing overfitting through adaptive loss balancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional multitask training is used with fixed task weights, then the training process is simple, but gradient magnitude imbalances cause suboptimal training dynamics and overfitting
Solution Approach 1:
The patent implements dynamic task weight adjustment by computing gradient norms for each task and using them to update weights iteratively during training. This transforms the static weight assignment into a dynamic process that adapts to gradient magnitude variations, resolving the contradiction between training reliability and method complexity.
Solution Approach 2:
The system computes gradient norms as feedback signals to monitor training progress for each task and uses this feedback to adjust task weights. This closed-loop feedback mechanism ensures that tasks with larger gradient magnitudes receive smaller weights and vice versa, improving training dynamics while maintaining manageable complexity through automated weight adjustment.
2Reliability
If task weights are manually tuned through exhaustive grid search, then training balance can be achieved, but computational cost and time requirements increase significantly
Solution Approach 1:
The patent implements self-service by enabling the training system to automatically adjust task weights based on computed gradient norms without requiring manual intervention or exhaustive grid search. The system serves itself by dynamically computing optimal weights through the gradient normalization process, achieving training balance while maintaining high productivity.
Solution Approach 2:
The system changes the task weight parameters dynamically during training based on gradient norm computations. Instead of fixing weights beforehand through time-consuming grid search, the parameters are continuously adjusted according to the actual training dynamics, achieving both training balance and computational efficiency.
3Reliability
If gradient norms are not normalized across tasks, then training process is straightforward, but tasks with different loss scales train at different rates leading to overfitting
Solution Approach 1:
The patent applies equipotentiality by normalizing gradient norms across tasks to equalize their training rates. This creates a balanced training landscape where all tasks contribute equally to the optimization process, preventing overfitting while the additional gradient processing remains computationally manageable through efficient norm computations.
Data Source
AI summary
Systems and methods for training a multitask network is disclosed. In one aspect, training the multitask network includes determining a gradient norm of a single-task loss adjusted by a task weight for each task, with respect to network weights of the multitask network, and a relative training rate for the task based on the single-task loss for the task. Subsequently, a gradient loss function, comprising a difference between (1) the determined gradient norm for each task and (2) a corresponding target gradient norm, can be determined. An updated task weight for the task can be determined and used in the next iteration of training the multitask network, using a gradient of the gradient loss function with respect to the task weight for the task.