Definition
Multitask optimization involves training a single model to handle several distinct but related tasks at once. By sharing intermediate representations across tasks, the model can learn more generalized features that benefit all associated objectives. This approach often leads to improved performance compared to training separate models for each task, as it reduces overfitting and leverages commonalities between tasks. It is particularly useful when data for individual tasks is limited or when computational efficiency is a priority.
Summary
A training strategy where a model is optimized to perform multiple related tasks simultaneously.
Key Concepts
- Shared representation
- Task-specific heads
- Gradient balancing
- Transfer learning
Use Cases
- Joint object detection and segmentation
- Multi-label classification
- Speech recognition and language modeling