Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

Deep multitask networks, in which one neural network produces multiple predictive outputs, are more scalable and often better regularized than their single-task counterparts.
Such advantages can potentially lead to gains in both speed and performance, but multitask networks are also difficult to train without finding the right balance between tasks.
We present a novel gradient normalization (GradNorm) technique which automatically balances the multitask loss function by directly tuning the gradients to equalize task training rates.
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 over single 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 which 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 hope to 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.
Single-task learning in computer vision has enjoyed much success in deep learning, with many models now performing at or beyond human accuracies for a wide array of tasks.
However, a system that strives for full scene understanding cannot focus on one problem, but needs to perform many diverse perceptual tasks simultaneously.
Such systems must also be efficient, especially within the restrictions of limited compute environments in embedded systems such as smartphones, wearable devices, and robots/drones.
Multitask learning most naturally lends itself to this problem by sharing weights amongst different tasks within the same model and producing multiple predictions in one forward pass.
Such networks are not only scalable, but the shared features within these networks tend to be better regularized and boost performance as a result.
In the ideal limit, we can thus have the best of both worlds with multitask networks: both more efficiency and higher performance.The key difficulty in multitask learning lies in the balancing of tasks, and perhaps the simplest way to control this balance is to choose the correct joint loss function.
In practice, the multitask loss function is often assumed to be linear in the single task losses, L = i w i L i , where the sum runs over T tasks.
The challenge is then to find the best value for each w i that balances the contribution of each task for optimal model training.
Our proposed method is furthermore an adaptive method, allowing w i to vary with the training step t, and so w i = w i (t).Our
key insight lies in the observation that these w i (t) influence training only because they control the magnitude of the gradients generated from task i. As
such, manipulating the gradient norms themselves would be a more direct way to control the training dynamics. More
specifically, we propose a simple heuristic that penalizes the network when backpropagated gradients from any task are too large or too small. The
correct balance is struck when tasks are training at similar rates; if task i is training relatively quickly, then its weight w i (t) should decrease relative to other task weights Figure 1 : Gradient Normalization. Imbalanced
gradient norms (left) result in suboptimal training within a multitask network, so we implement a novel gradient loss L grad (right) which detects such imbalances in gradient norms amongst tasks and tunes the weights in the loss function to compensate. We illustrate
here a simplified case where such balancing results in equalized gradient norms, but in general some tasks may need higher or lower gradient norms relative to other tasks for optimal task balancing (discussed further in Section 3). w j (t)| j =i
to allow other tasks more influence on the network. Our method can
be said to be a form of batch normalization BID10 ) for backpropagation, ensuring that gradients from each task per batch lie on a common statistical scale. We will show that
, when implemented, gradient normalization leads to across-the-board improvements in accuracy and suppresses overfitting.Our main contributions to the field of multitask learning are as follows:1. An attractively
simple heuristic for multitask loss balancing involving training rate equalization, which is implemented through a novel gradient loss function. 2. A simplification
to exhaustive grid search (which has compute complexity O(N T ) for N grid points in one dimension) that only involves tuning one robust hyperparameter. 3. Demonstration that
direct interaction with gradients provides a powerful way of reasoning about multitask learning.
Gradient normalization acts as a good model regularizer and leads to superb performance in multitask networks by operating directly on the gradients in the network.
GradNorm is driven by the attractively simple heuristic of rate balancing, and can accommodate problems of varying complexities within the same unified model using a single hyperparameter representing task asymmetry.
A GradNorm network can also be used to quickly extract optimal fixed task weights, removing the need for exhaustive grid search methods that become exponentially more expensive with the number of tasks.
We hope that our work has not only introduced a new methodology for quickly balancing multitask networks, but also has shown how direct gradient manipulation can be a powerful way to reason about task relationships within a multitask framework.
We show how you can boost performance in a multitask network by tuning an adaptive multitask loss function that is learned through directly balancing network gradients.