When to Choose Multi-Task vs Separate Models

When Multi-task Makes Sense
Related tasks with shared structure: Object detection and instance segmentation both need to understand object boundaries. Depth estimation and surface normal prediction both need geometric understanding. These pairs naturally benefit from shared features.
Limited data scenarios: If Task A has millions of examples but Task B has thousands, training jointly transfers knowledge from A to B. The shared backbone learns features from A that help B generalize better.
Latency constrained serving: When users need multiple outputs simultaneously and latency budget is tight, a single multi-task forward pass beats multiple single-task calls.
When Separate Models Win
Conflicting task requirements: If tasks need fundamentally different features, forcing them through shared layers hurts both. Separate models let each task optimize independently.
Different update frequencies: If one task needs daily retraining and another needs monthly, coupled training creates unnecessary overhead. Separate models update independently.
Independent failure domains: If a bug in one task should not affect another, separate models provide isolation. Multi-task models propagate problems across all outputs.
Decision Framework
Start with separate models. Measure single-task baselines for each task. Then train a multi-task model and compare. If multi-task matches or exceeds all baselines, adopt it for the efficiency gains. If any task regresses significantly, keep separate models.
💡 Key Insight: Multi-task learning is an optimization, not a default. Always establish single-task baselines first. Only adopt multi-task if it matches performance while providing efficiency benefits.

💡 Key Takeaways

✓Multi-task wins for related tasks, limited data scenarios, and latency-constrained multi-output serving

✓Separate models win when tasks conflict, update at different frequencies, or need failure isolation

✓Decision framework: establish single-task baselines first, adopt multi-task only if it matches all baselines

✓Multi-task is an optimization for efficiency, not a default approach

📌 Interview Tips

1Interview Tip: Frame multi-task as a choice requiring justification - start by asking if tasks are related enough to benefit

2Interview Tip: Mention baseline comparison as mandatory - you cannot know if multi-task helps without single-task baselines

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