引导式测验 · 中等 等级

多任务网络测验中的硬参数共享

测试共享主干、任务头、软共享对比、损失权重、梯度竞争和特定任务评估。

相关引导路径多任务网络中的硬参数共享
问题 1 的 8

What does hard parameter sharing mean?

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  1. What does hard parameter sharing mean?
    • Each task has a separate full network with a similarity penalty
    • Tasks share labels but no model parameters
    • One output head predicts every task without task identity
    • Tasks share a common hidden trunk and use task-specific output heads
  2. How does hard sharing differ from soft parameter sharing?
    • Soft sharing keeps separate task parameters and encourages them to remain similar
    • Soft sharing always uses one shared trunk
    • Hard sharing stores no parameters
    • The two terms mean the same architecture
  3. Why can hard sharing reduce overfitting for related tasks?
    • Each task receives more independent parameters
    • Shared layers learn features that must serve multiple tasks
    • The model avoids training data
    • All tasks are forced to use identical output labels
  4. How are task losses commonly combined for joint training?
    • By choosing one task and ignoring the others
    • By averaging labels across tasks
    • As a weighted sum in a shared objective
    • By using only the smallest loss
  5. What can happen if one task’s loss or gradients dominate?
    • All task heads disappear
    • The tasks become independent
    • 该模型会自动平衡所有梯度
    • 共享主干可能会优先处理某些任务，而其他任务则得不到充分服务
  6. 硬参数共享在什么情况下最合适？
    • 当任务可以从有用的共享表示中受益时
    • 当所有任务都互不相关且不兼容时
    • 当一个任务绝不能影响另一个任务时
    • 当只有一个任务时
  7. 任务特定头部通常做什么？
    • 存储所有训练样本
    • 将共享特征映射到该任务的输出
    • 平衡其他所有任务的梯度
    • 为每个标记选择使用哪个隐藏主干
  8. 训练多任务模型时应监控什么？
    • 仅监控总参数量
    • 仅监控最大任务的准确率
    • 每个任务的性能以及共享更新对每个任务是有益还是有害
    • 仅监控输出头部的数量