Hard vs Soft Parameter Sharing Strategies

Hard Parameter Sharing

In hard parameter sharing, all tasks share the same backbone network. Each task has its own small output head, but the heavy lifting happens in shared layers. This is the most common approach because it is simple and memory efficient.

How it works: Input flows through shared convolutional or transformer layers. At some depth, the network branches into task specific heads. Each head typically adds 5-10% to total parameters while the shared backbone contributes 90%+.

When it works well: Tasks that need similar low-level features benefit from hard sharing. Object detection and segmentation both need edge detection and texture understanding. Sharing these early layers helps both tasks.

Soft Parameter Sharing

In soft parameter sharing, each task has its own network, but the networks are encouraged to stay similar through regularization. Parameters are not literally shared but are constrained to not diverge too far.

How it works: Each task has full parameters. During training, add a penalty term that measures how different the networks have become. The penalty pushes networks toward similar weights without forcing them to be identical.

When it works well: Tasks that need different feature representations benefit from soft sharing. One task might need fine grained texture; another might need global shape. Soft sharing allows each to specialize while still transferring useful knowledge.

Choosing Your Strategy

Default to hard sharing when tasks are closely related and you want maximum efficiency. Hard sharing uses 50-80% less memory than separate models.

Use soft sharing when tasks conflict or need specialized representations. Soft sharing uses more memory but avoids the negative transfer that hard sharing can cause when tasks compete for shared capacity.