![]() In a nutshell, attention in deep learning can be broadly interpreted as a vector of importance weights: in order to predict or infer one element, such as a pixel in an image or a word in a sentence, we estimate using the attention vector how strongly it is correlated with (or “ attends to” as you may have read in many papers) other elements and take the sum of their values weighted by the attention vector as the approximation of the target. One word "attends" to other words in the same sentence differently. The color term describes the food, but probably not so much with “eating” directly. When we see “eating”, we expect to encounter a food word very soon. Similarly, we can explain the relationship between words in one sentence or close context. However, the sweater and blanket at the bottom would not be as helpful as those doggy features. We expect to see a pointy ear in the yellow box because we have seen a dog’s nose, another pointy ear on the right, and Shiba’s mystery eyes (stuff in the red boxes). Given a small patch of an image, pixels in the rest provide clues what should be displayed there. now how about the snowy background and the outfit?), and then adjust the focal point or do the inference accordingly. look at the pointy ear in the yellow box) while perceiving the surrounding image in “low resolution” (i.e. The credit of the original photo goes to Instagram visual attention allows us to focus on a certain region with “high resolution” (i.e. arXiv: 1706.03762 (2017).Attention is, to some extent, motivated by how we pay visual attention to different regions of an image or correlate words in one sentence. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., & Gomez, A. Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems. Advances in Neural Information Processing Systems. Teaching machines to read and comprehend. Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets. Sukhbaatar, Sainbayar, Jason Weston, and Rob Fergus. Show, attend and tell: Neural image caption generation with visual attention. Describing Multimedia Content using Attention-based Encoder–Decoder Networks. Cho, Kyunghyun, Aaron Courville, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. Show and tell: A neural image caption generator. If interested, you could check out papers below. There are many variants in the cutting-edge researches, and they basically differ in the choice of score function and attention function, or of soft attention and hard attention (whether differentiable). Implementing your own attention layer is encouraged. We hope you understand the reason why attention is one of the hottest topics today, and most importantly, the basic math behind attention. Thus, different styles may result in different performance. According to equation (4), both styles offer the trainable weights (W in Luong’s, W1 and W2 in Bahdanau’s). We need attention mechanism to be trainable.Once context vector is computed, attention vector could be computed by context vector, target word, and attention function f.Equation (1) demonstrates how to compute a single value given one target word and a set of source word. So we will have a 2D matrix whose size is # of target words multiplied by # of source words. ![]()
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