Overcoming the Cold Start Problem: How to Make New Tasks Tractable

  1. the cars look different and
  2. the way those cars are repaired is also different.
  1. Input shift: This happens when the inputs — in our case, images — that our AI model sees look different. There’s a wide spectrum covering what we would call a car and they might look very different.
  2. Conditional shift: This happens because the repair decisions, which are conditional on the damage to the car, are different around the world. To put that another way — if you have the exact same accident in Thailand, the UK and Poland, the car repairs will be different in each country. Our AI model can’t make the right decision unless it knows about these specifics.
  3. Output shift: The happens when you use the outputs of one domain for another. For example, in our case, the distribution of our labels — repair or don’t repair — would be very different for different countries. An AI model trained on Japanese data might be inclined to repair everything just because that’s what it has mainly seen from the data, even when used in other countries, where the methodology is very different.
  1. Bagging: In bagging, the weak learners are trained independently from each other in parallel and are then combined by following some kind of deterministic averaging process.
  2. Boosting: in boosting, the weak learners are trained sequentially in a very adaptative way — meaning a weak learner is trained to focus on the misclassified examples of the previous weak learners — and then they are combined using a deterministic strategy.
  3. Stacking: Similar to bagging, the weak learners are trained in parallel and independently from each other, but unlike the previous two methods, the weak learners are combined by training a meta-model on top of them instead of using a deterministic strategy.



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