After the model converges we compute for each of the 500 pivots its 10 nearest neighbor and compute the percentage of these neighbors that belong to the same class, PosPiv or NegPiv, as the pivot.
while in CL the prediction task is fixed but the trained algorithm is exposed to increasingly more complex training examples in subsequent stages, in TRL the algorithm is trained to solve increasingly more complex tasks in subsequent stages, but the training data is kept fixed across the stages.