1.Origin Transformers are an important deep learning architecture that originated in the fields of computer science and artificial intelligence. They have achieved remarkable success in natural language processing and other sequential data tasks. The history and evolution of this architecture are worth exploring. The story of Transformers began in 2017, when Vaswani et al. first … Read more
Transformer is a model that utilizes the attention mechanism to improve the training speed of models. For more information about the attention mechanism, you can refer to this article (https://zhuanlan.zhihu.com/p/52119092). The transformer can be said to be a deep learning model that is entirely based on the self-attention mechanism, as it is suitable for parallel … Read more
Introduction In recent years, deep learning has made tremendous progress in the field of Natural Language Processing (NLP), and the Transformer model is undoubtedly one of the best. Since the Google research team proposed the Transformer model in their paper “Attention is All You Need” in 2017, it has become the cornerstone for many NLP … Read more
Biomimetic Brain Attention Model -> Resource Allocation The deep learning attention mechanism is a biomimetic of the human visual attention mechanism, essentially a resource allocation mechanism. The physiological principle is that human visual attention can receive high-resolution information from a specific area in an image while perceiving its surrounding areas at a lower resolution, and … Read more