Multi-Task Model Combination Pattern

A Multi-Task Model Combination Pattern is a model combination pattern that uses a shared encoder with multiple task-specific heads to enable mutual task regularization.

• AKA: Multi-Task Sharing Pattern, MTL Pattern, Shared Representation Pattern.
• Context:
  • It can typically support Multi-Task Related Task Pairs with multi-task shared representations.
  • It can typically enable Multi-Task Joint Training through multi-task loss aggregations.
  • It can often enhance Multi-Task Model Generalization via multi-task inductive biases.
  • It can often reduce Multi-Task Training Time through multi-task parameter sharings.
  • It can range from being a Hard Multi-Task Model Combination Pattern to being a Soft Multi-Task Model Combination Pattern, depending on its multi-task sharing degree.
  • It can range from being a Homogeneous Multi-Task Model Combination Pattern to being a Heterogeneous Multi-Task Model Combination Pattern, depending on its multi-task type diversity.
  • It can range from being a Symmetric Multi-Task Model Combination Pattern to being a Asymmetric Multi-Task Model Combination Pattern, depending on its multi-task importance weighting.
  • It can range from being a Static Multi-Task Model Combination Pattern to being a Dynamic Multi-Task Model Combination Pattern, depending on its multi-task selection strategy.
  • ...
• Examples:
  • Natural Language Processing Multi-Task Patterns, such as:
    • POS-NER Multi-Task Model Combination Pattern for syntactic-semantic joint learning.
    • Translation-Summarization Multi-Task Pattern for cross-lingual understanding.
    • Sentiment-Topic Multi-Task Pattern for opinion analysis.
  • Computer Vision Multi-Task Patterns, such as:
    • Detection-Segmentation Multi-Task Pattern for object understanding.
    • Depth-Normal Multi-Task Pattern for 3D scene understanding.
    • Classification-Localization Multi-Task Pattern for visual recognition.
  • Speech Processing Multi-Task Patterns, such as:
    • ASR-Speaker-Diarization Multi-Task Pattern for audio understanding.
    • Speech-Emotion Multi-Task Pattern for affective computing.
  • Multi-Modal Multi-Task Patterns, such as:
    • Vision-Language Multi-Task Pattern for cross-modal learning.
    • Audio-Visual Multi-Task Pattern for multimedia understanding.
  • ...
• Counter-Examples:
  • Single-Task Model, which focuses on one task without sharing.
  • Transfer Learning Pattern, which adapts sequentially rather than jointly.
  • Model Ensemble Pattern, which combines outputs rather than representations.
• See: Model Combination Pattern, Multi-Task Learning Task, Multi-Task Deep Neural Network (MT-DNN), Shared Encoder Architecture, Task Head, Joint Training, Parameter Sharing.

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