Saliency Propagation from Simple to Difficult ( Supplementary Material )
نویسندگان
چکیده
When the iteration proceeds, the size l of labeled set L will grow larger and larger, so inverting the l × l matrix KL,L (KL,L is the sub-matrix of K corresponding to L) at scratch under each iteration is very inefficient. Here we tackle this computational issue by incrementally updating K−1 L,L based on the previous inverting result. As our submission, T is used to denote the curriculum set with size q, and KT ,L, KL,T , KL,L are sub-matrices of the kernel matrix K indexed by the associated subscripts. After one iteration, the kernel matrix on the labeled set is updated by
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