What is Multi-Task Learning?

Definition
Multi-task Learning trains a single model to perform multiple related tasks simultaneously. Instead of building separate models for object detection, segmentation, and depth estimation, one model handles all three by sharing learned representations across tasks.
Why Multi-task Learning Works
Related tasks share underlying structure. Detecting objects and estimating their depth both require understanding scene geometry. When tasks share a backbone network, features learned for one task help the others. This is called positive transfer.
Efficiency gain: Three separate models might use 300MB each (900MB total). A multi-task model uses 100MB for shared layers plus 20MB per task head (160MB total). You get 5x memory savings while maintaining or improving accuracy.
Architecture Overview
Shared backbone: Convolutional or transformer layers that process raw input. These layers learn features useful for all tasks.
Task-specific heads: Small networks branching from the backbone. Each head specializes in one task - classification, detection, segmentation.
Joint training: All tasks train together. Gradients from each task flow back through the shared backbone, creating representations that balance all task requirements.
When Multi-task Makes Sense
Multi-task learning helps when tasks are related and data is limited. If you have abundant data for each task, separate models may perform better. The sweet spot: related tasks where some have limited data that benefits from transfer.

💡 Key Takeaways

✓Multi-task learning shares representations across related tasks, enabling positive transfer

✓Memory savings of 5x or more compared to separate models through shared backbone layers

✓Architecture has shared backbone for common features plus task-specific heads for each output

✓Best when tasks are related and some tasks have limited data that benefits from transfer

📌 Interview Tips

1Interview Tip: Explain multi-task as an efficiency technique - mention specific memory/compute savings with numbers

2Interview Tip: Frame positive transfer as learning shared structure - geometry understanding helps both detection and depth

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