10. Multi-Task and Multi-Objective Losses
Multi-task losses are used when a model is trained to perform multiple tasks simultaneously. The loss is a weighted sum of the individual task losses.
10.1 Weighted Sum of Losses
The simplest approach is to use a weighted sum of the individual task losses. The weights can be set manually based on the importance of each task.
Real-World Applications: Multi-task classification, multi-task regression, and any scenario where multiple objectives must be balanced (e.g., autonomous driving where a model must handle detection, segmentation, and depth estimation simultaneously).
10.2 Uncertainty Weighting
Uncertainty Weighting is a technique for automatically learning the weights of the individual task losses based on the uncertainty (variance) of each task. The loss automatically balances the tasks based on their uncertainty.
Real-World Applications: Multi-task learning benchmarks (improving performance on multi-task learning tasks), multi-task neural networks (widely used in computer vision and NLP), and any domain where tasks have different scales and uncertainties.
10.3 Pareto Multi-Task Learning
Pareto Multi-Task Learning (MTL) is a framework that aims to find a set of Pareto-optimal solutions, where no task can be improved without degrading another task.
Real-World Applications: Multi-objective optimization, applications where tasks are in conflict (e.g., accuracy vs. efficiency, fairness vs. performance), and any domain where trade-offs must be explicitly managed.
Applications
- Multi-task classification (Weighted Sum).
- Multi-task regression (Uncertainty Weighting).
- Multi-objective optimization (Pareto MTL).
Strengths and limitations
| Strengths | Limitations |
|---|---|
| Simple and flexible. | Requires careful weight tuning. |
| Uncertainty weighting is automatic. | Uncertainty weighting requires variance estimation. |
| Pareto MTL finds optimal trade-offs. | Pareto MTL is computationally expensive. |
Table 12: Strengths and limitations of multi‑task losses. Weighted sum is simple and flexible but requires manual tuning of task weights. Uncertainty weighting automates balancing via variance estimation. Pareto MTL finds optimal trade‑offs but is computationally expensive and harder to implement in practice.