similarity learning

• why similarity learning?

  • we can’t solve all problems using regression or classification
  • example - face recognition
  • in general tasks like comparison, ranking and recognition need something more!
  • if we used regression to predict a similarity score between 2 faces for example we would need to train our model for all possible pairs of faces which is impossible!
  • if we used classification to recognize a person, we would need to train on every person which is not scalable!
  • if we did a binary classification - {similar=0.2, different=0.8} we would need to run the model for all pairs of samples and the ranking would not be consistent meaning if A is similar to B and B is similar to C that does not mean A is similar to C!

• how to define and quantify similarity?

  • a positive function that returns a small value when x1 and x2 are similar and a large value when both are dissimilar
  • we learn inverse similarity rather than similarity itself
  • to determine matching pairs we use a threshold T

solution - constraint the distance D to be a metric - ie. produce a metric space

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