Soft Attention是参数化的（Parameterization），因此可导，可以被嵌入到模型中去，直接训练。梯度可以经过Attention Mechanism模块，反向传播到模型其他部分。 也有称作TOP-down Attention。 3. Kelvin Xu等人与2015年发表论文《Show, Attend and Tell: Neural Image Caption Generation with Visual Attention》，在Image Caption中引入了Attention，当生成第i个关于图片内容描述的词时，用Attention来关联与i个词相关的图片的区域。 Attention Mechanism分类. So instead of a weighted average, hard attention uses as a sample rate to pick one as the input to the LSTM. It is very easy conceptually, as it only requires indexing. in their paper and described by Xu et al. Hard attention is when, instead of weighted average of all hidden states, we use attention scores to select a single hidden state. Hard Attention： 相反，Hard Attention是一个随机的过程。
Hard attention. In soft attention, we compute a weight for each , and use it to calculate a weighted average for as the LSTM input. Hard attention for images has been known for a very long time: image cropping. adds up to 1 which can be interpreted as the probability that is the area that we should pay attention to. Both soft and hard attention can be used in memory … For hard attention it is less to do with only some of the inputs are used and others are left out, but more so that the decision itself of which inputs are used and left-out is also computed with a neural network.
Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. 1. @bikashg your understanding for the soft attention is correct.
With hard attention reinforcement learning (RL) can be used to train the model while soft attention, which uses a softmax function, can be trained using the usual gradient descent plus backprop algorithm. Hard attention is a stochastic process: instead of using all the hidden states as an input for the decoding, the system samples a hidden state yi with the probabilities si.
2.1 soft Attention 和Hard Attention. the score-function estimator (REINFORCE), briefly mentioned in my previous post. Referred by Luong et al. Hard vs Soft attention. 2. Let y∈[0,H−h] and x∈[0,W−w] be coordinates in the image space; hard-attention can be implemented in Python (or Tensorflow) asThe only problem with the above is that it is non-differentiable; to learn the parameters of the model, one must resort to e.g.
in their paper, soft attention is when we calculate the context vector as a weighted sum of the encoder hidden states as we had seen in the figures above.
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Hard attention. In soft attention, we compute a weight for each , and use it to calculate a weighted average for as the LSTM input. Hard attention for images has been known for a very long time: image cropping. adds up to 1 which can be interpreted as the probability that is the area that we should pay attention to. Both soft and hard attention can be used in memory … For hard attention it is less to do with only some of the inputs are used and others are left out, but more so that the decision itself of which inputs are used and left-out is also computed with a neural network.
Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. 1. @bikashg your understanding for the soft attention is correct.
With hard attention reinforcement learning (RL) can be used to train the model while soft attention, which uses a softmax function, can be trained using the usual gradient descent plus backprop algorithm. Hard attention is a stochastic process: instead of using all the hidden states as an input for the decoding, the system samples a hidden state yi with the probabilities si.
2.1 soft Attention 和Hard Attention. the score-function estimator (REINFORCE), briefly mentioned in my previous post. Referred by Luong et al. Hard vs Soft attention. 2. Let y∈[0,H−h] and x∈[0,W−w] be coordinates in the image space; hard-attention can be implemented in Python (or Tensorflow) asThe only problem with the above is that it is non-differentiable; to learn the parameters of the model, one must resort to e.g.
in their paper, soft attention is when we calculate the context vector as a weighted sum of the encoder hidden states as we had seen in the figures above.
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