GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks (1711.02257v4)

Abstract: Deep multitask networks, in which one neural network produces multiple predictive outputs, can offer better speed and performance than their single-task counterparts but are challenging to train properly. We present a gradient normalization (GradNorm) algorithm that automatically balances training in deep multitask models by dynamically tuning gradient magnitudes. We show that for various network architectures, for both regression and classification tasks, and on both synthetic and real datasets, GradNorm improves accuracy and reduces overfitting across multiple tasks when compared to single-task networks, static baselines, and other adaptive multitask loss balancing techniques. GradNorm also matches or surpasses the performance of exhaustive grid search methods, despite only involving a single asymmetry hyperparameter $\alpha$. Thus, what was once a tedious search process that incurred exponentially more compute for each task added can now be accomplished within a few training runs, irrespective of the number of tasks. Ultimately, we will demonstrate that gradient manipulation affords us great control over the training dynamics of multitask networks and may be one of the keys to unlocking the potential of multitask learning.

• The paper introduces GradNorm, an algorithm that autonomously balances multitask loss functions by tuning gradients to equalize training rates.
• GradNorm improves accuracy and reduces overfitting across both regression and classification tasks while minimizing exhaustive hyperparameter tuning.
• The approach enhances scalability in deep multitask networks, streamlining training on both synthetic and real-world datasets.

Gradient Normalization for Multitask Networks

In the paper titled "Submission and Formatting Instructions for ICML 2018", the authors present a novel technique called Gradient Normalization (GradNorm) designed to tackle the inherent challenges of training deep multitask networks. Multitask learning involves a single neural network producing multiple predictive outputs, with potential advantages in scalability and regularization compared to single-task networks. However, training these networks is often complex due to the need to balance different tasks appropriately.

Contribution

The primary contribution of this paper is the introduction of GradNorm, an algorithm to autonomously balance the multitask loss function. This is achieved by tuning the gradients to equalize the training rates of different tasks. The authors validate GradNorm across various network architectures, spanning both regression and classification tasks, and tested it on both synthetic and real datasets.

Results

The empirical results highlight several advantages of GradNorm:

1. Improved Accuracy and Reduced Overfitting: GradNorm demonstrates superior performance compared to single-task networks, static baselines, and other adaptive multitask loss balancing techniques.
2. Efficiency: The technique reduces the need for exhaustive hyperparameter tuning, specifically the need for grid search methods. Despite the involvement of a single hyperparameter $\alpha$, GradNorm consistently matches or exceeds the performance of methods that require extensive computational resources.
3. Scalability: The approach is scalable as it simplifies the traditionally complex process of tuning for each task added to the network. Instead of incurring exponentially more compute, the process is streamlined to a few training runs.

Implications

The implications of GradNorm are significant for the field of multitask learning. By providing better control over training dynamics through gradient manipulation, the technique holds promise to substantially enhance the efficacy and efficiency of multitask networks. This advancement paves the way for more sophisticated implementations of multitask learning in practical applications where computational resources and time are critical factors.

Theoretical and Practical Impact

On a theoretical level, the introduction of GradNorm underscores the importance of gradient tuning mechanisms in training deep learning models. This could spur further research into gradient-based approaches for optimizing neural network training protocols. Practically, the robustness and efficiency of GradNorm make it a valuable tool for developers and researchers working on multitask learning systems, potentially reducing development time and computational costs in real-world applications.

Future Developments

Future research could expand on the findings of this paper by exploring:

• Adaptation to Various Network Architectures: Investigating the applicability of GradNorm to a broader range of neural network architectures beyond those tested.
• Hyperparameter Optimization: Developing enhanced techniques for dynamically adjusting the $\alpha$ hyperparameter in increasingly complex scenarios.
• Real-World Implementation: Assessing the performance of GradNorm in diverse real-world applications, particularly those involving large-scale, heterogeneous datasets.

In summary, this paper provides a thorough investigation into GradNorm, highlighting its potential to address the balance and training rate issues in multitask networks. The methodology, results, and implications discussed herein could inspire further advancements and applications of multitask learning strategies.