Practical Convolutional Neural Networks

Soft Attention

As introduced in the preceding sub-section on hard attention, soft attention uses RL to progressively train and determine where to seek next (control problem).

There exist two major problems with using the combination of hard attention and RL to achieve the required objective:

• It becomes slightly complicated to involve RL and train an RL agent and an RNN/deep network based on it separately.
• The variance in the gradient of the policy function is not only high (as in A3C model), but also has a computational complexity of O(N), where N is the number of units in the network. This increases the computation load for such approaches massively. Also, given that the attention mechanism adds more value in overly long sequences (of words ...