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Downstream task

As explained in Deep Learning with R, Third Edition

A downstream task is the target application for which a pretrained model or representation is subsequently adapted or evaluated.

A downstream task is the target application for which a pretrained model or representation is adapted or evaluated. Instead of training from scratch on one dataset, a foundation model can provide a rich representation that is specialized for the final task. section 16.4.2 section 14.6

A downstream task typically follows this pattern:

  • Start with parameters or representations learned from broad, usually unlabeled data. section 16.4.2 section 14.6
  • Adapt the pretrained model to a particular application rather than specializing it during initial training. section 16.4.2
  • Use the resulting specialized model for a task such as text classification. section 14.6

Text classification after pretraining

A sequence model can first learn from large amounts of unlabeled text through pretraining, then transfer its learned parameters to a text-classification downstream task. section 14.6

How Deep Learning with R, Third Edition covers it

How deep it goes
Defines task transfer and specialization but stops short of detailing adaptation procedures, architectures, losses, or evaluation methods.
Examples
Uses text classification as the concrete example following sequence-model pretraining.
What it assumes
Assumes familiarity with pretrained models, representations, and learning from unlabeled data.
What you can do after
Apply it via fine-tuning a pretrained model for a specific final application.
How it connects
Connects downstream tasks to foundation models, pretraining, self-supervised learning, embeddings, sequence models, and text classification.
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