(2017) add the adversarial losses in multi-task RNNs, which can alleviate the interference among tasks by finding a common feature space for tasks.
more concretely, we introduce a coupling coefficient c (k) ij between capsule i in l-th layer and capsule j in class capsule (l + 1)-th layer for task k, which is the result of a softmax function on b Then, instantiated parameter v ij is restricted in range [0, 1], which represents the probability that capsule i belongs to class capsule j in task k. And it is update by the algorithm is described in Algorithm 3.5.