In recent years, artificial intelligence technology has achieved world-renowned results. Among them, natural language processing (NLP) ) and computer vision are particularly prominent. In these fields, a model called Transformer has gradually become a research hotspot, and innovative results with it as its core are emerging one after another. This article will explore how Transformer leads the flourishing of AI technology from aspects such as its principles, applications, and industrial practices.
Before introducing Transformer, you need to understand its background knowledge-Recurrent Neural Network (RNN) and Long Short-term Memory Network ( LSTM). RNN has the problems of gradient disappearance and gradient explosion when processing sequence data, which makes it perform poorly in long sequence tasks. In order to solve this problem, LSTM came into being and effectively alleviated the vanishing and exploding gradient problems by introducing a gating mechanism. In order to solve this problem, LSTM came into being and effectively alleviated the vanishing and exploding gradient problems by introducing a gating mechanism.
In 2017, the Google team launched a brand new model-Transformer. Its core idea is to use the self-attention (Self-Attention) mechanism to replace the traditional of recurrent neural networks. Transformer has achieved remarkable results in the field of NLP, especially in machine translation tasks, and its performance far exceeds LSTM. This model has been widely used in natural language processing tasks such as machine translation and question answering systems.
Transformer consists of two parts: encoder (Encoder) and decoder (Decoder). The encoder is responsible for mapping the input sequence into a series of vectors, and the decoder is responsible for mapping the input sequence into a series of vectors. The output of the controller and the known partial output are used to predict the next output. In sequence-to-sequence tasks, such as machine translation, the encoder maps the source language sentence into a series of vectors, and the decoder generates the target language sentence based on the output of the encoder and the known partial output.
"(1) Encoder: The encoder consists of multiple identical layers, and each layer includes two sub-layers: multi-head self-attention mechanism and positional fully connected feed-forward network." Note: The paragraph in this article is about the structure of the encoder in the neural network. The original meaning should be retained after modification, and the number of words should not exceed 114.
The decoder is composed of multiple identical layers, each layer including three sub-layers: multi-head attention mechanism, encoder-decoder attention mechanism and forward pass network. The multi-head self-attention mechanism, encoder-decoder attention mechanism and position encoder are its key components, which can implement the decoder attention mechanism while covering position and fully connected feed-forward networks. In addition, the decoder's attention mechanism and position encoder can also improve its performance through network connections that can be used throughout the network
The self-attention mechanism is The core of Transformer, its calculation process is as follows:
(1) Calculate three matrices of Query, Key and Value. These three matrices are obtained by linear transformation of the input vector. .
(2) Calculate the attention score, which is the dot product of Query and Key.
(3) Divide the attention score by a constant to obtain the attention weight.
(4) Multiply the attention weight and Value to obtain the weighted output.
(5) Perform linear transformation on the weighted output to obtain the final output.
Transformer has achieved remarkable results in the field of NLP, mainly including the following aspects:
（ 1) Machine translation: Transformer achieved the best results at the time in the WMT2014 English-German translation task.
(2) Text classification: Transformer performs well in text classification tasks, especially in long text classification tasks, its performance far exceeds LSTM.
(3) Sentiment analysis: Transformer can capture long-distance dependencies and therefore has a high accuracy in sentiment analysis tasks.
With the success of Transformer in the field of NLP, researchers began to apply it to the field of computer vision and achieved the following results:
(1) Image Classification: Transformer-based models have achieved good results in the ImageNet image classification task.
(2) Target detection: Transformer performs well in target detection tasks, such as DETR (Detection Transformer) model.
(3) Image generation: Transformer-based models such as GPT-3 have achieved impressive results in image generation tasks.
Chinese scholars have achieved fruitful results in the field of Transformer, such as:
(1) The ERNIE model proposed by Tsinghua University improves the performance of pre-trained language models through knowledge enhancement.
(2) The BERT-wwm model proposed by Shanghai Jiao Tong University improves the performance of the model on Chinese tasks by improving the pre-training objectives.
Chinese enterprises have also achieved remarkable results in the application of Transformer, such as:
(1) The ERNIE model proposed by Baidu is used in search engines, speech recognition and other fields.
(2) The M6 model proposed by Alibaba is applied to e-commerce recommendation, advertising prediction and other businesses.
Transformer is increasingly widely used in the industry, mainly including the following aspects:
(1) Search engine: Use Transformer for semantic understanding and improve search quality.
(2) Speech recognition: Through the Transformer model, more accurate speech recognition is achieved.
(3) Recommendation system: Transformer-based recommendation model improves recommendation accuracy and user experience.
(1) Model compression and optimization: As the scale of the model continues to expand, how to compress and optimize the Transformer model has become a research hotspot.
(2) Cross-modal learning: Transformer has advantages in processing multi-modal data and is expected to make breakthroughs in the field of cross-modal learning in the future.
(3) Development of pre-training models: As computing power increases, pre-training models will continue to develop.
The above is the detailed content of Transformer leads the flourishing of AI: from algorithm innovation to industrial application, understand the future of artificial intelligence in one article. For more information, please follow other related articles on the PHP Chinese website!
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