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Introduction of
Structured Learning
Hung-yi Lee
Structured Learning
• We need a more powerful function f
• Input and output are both objects with
structures
• Object: sequence, list, tree, bounding box …
f : X Y
X is the space of Y is the space of
one kind of object another kind of object
In the previous lectures, the input and output are both vectors.
Example Application
• Speech recognition
• X : Speech signal (sequence) → Y: text (sequence)
• Translation
• X : Mandarin sentence (sequence) → Y: English sentence
(sequence)
• Syntactic Paring
• X : sentence → Y: parsing tree (tree structure)
• Object Detection
• X : Image → Y: bounding box
• Summarization
• X : long document → Y: summary (short paragraph)
• Retrieval
• X : keyword → Y: search result (a list of webpage)
Unified Framework
Training
• Find a function F
F : X Y R
• F(x,y): evaluate how compatible the
objects x and y is
Inference (Testing)
• Given an object x
~
y arg max F x , y
y Y
~
f : X Y f x y arg max F x , y
y Y
Unified Framework
– Object Detection
•Task description
• Using a bounding box to highlight the position of a
certain object in an image
• E.g. A detector of Haruhi
X : Image Y : Bounding Box
Haruhi
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