If you like it, please follow CV Circle ! The attention mechanism has become very popular in recent years. So, what are the origins and current developments of the attention mechanism? Let’s follow the author, Mr. Haohao, and take a look. Author of this article: Mr. Haohao, authorized for reprint Link: https://zhuanlan.zhihu.com/p/361893386 1 Background Knowledge … Read more
In the previous article, we learned about the basic seq2seq model, which processes the input sequence through an encoder, passes the calculated hidden state to a decoder, and then decodes it to obtain the output sequence. The block diagram is shown again below: The basic seq2seq model is quite effective for short and medium-length sentences … Read more
BiSeNet, which utilizes attention mechanisms in semantic segmentation, has two modules: the FFM module and the ARM module. Its implementation is quite straightforward, but the author has a deep understanding of the attention mechanism and proposes a novel feature fusion method through the FFM module. One Introduction Semiotic segmentation requires rich spatial information and a … Read more
“I found a bug in the attention formula, and no one has noticed it for eight years. All Transformer models, including GPT and LLaMA, are affected.” Recently, a statistical engineer named Evan Miller has stirred up a storm in the AI field with his statement. We know that the attention formula in machine learning is … Read more
The author of this article – Teacher Tom ▷ Doctorate from a double first-class domestic university, national key laboratory ▷ Published 12 papers at top international conferences, obtained 2 national invention patents, served as a reviewer for multiple international journals ▷ Guided more than ten doctoral and master’s students Research Areas: General visual-language cross-modal model … Read more
↓Recommended Follow↓ Transformers have fundamentally changed deep learning models since their introduction. Today, we will unveil the core concepts behind Transformers: the attention mechanism, encoder-decoder architecture, multi-head attention, and more. Through Python code snippets, you’ll gain a deeper understanding of its principles. 1. Understanding the Attention Mechanism The attention mechanism is a fascinating concept in … Read more
1. Overview The overall architecture of the Transformer has been introduced in the first section: Data must go through the following before entering the encoder and decoder: Embedding Layer Positional Encoding Layer The encoder stack consists of several encoders. Each encoder contains: Multi-Head Attention Layer Feed Forward Layer The decoder stack consists of several decoders. … Read more
Selected from TowardsDataScience Author: Raimi Karim Contributors: Gao Xuan, Lu This article visually explains the attention mechanism with several animated diagrams and shares four NMT architectures that have emerged in the past five years, along with intuitive explanations of some concepts mentioned in the text. For decades, statistical machine translation has dominated translation models [9], … Read more
Selected from arXiv Author: Graham Neubig Translation by Machine Heart Contributors: Li Zenan, Jiang Siyuan This article is a detailed tutorial on machine translation, suitable for readers with a background in computer science. According to Paper Weekly (ID: paperweekly), this paper comes from CMU LTI and covers various foundational knowledge of the Seq2Seq method, including … Read more
Machine Heart (Overseas) Original Author: Mos Zhang Participated by: Panda Machine Translation (MT) is the process of “automatically translating text from one natural language (source language) to another (target language)” using machines [1]. The idea of using machines for translation was first proposed by Warren Weaver in 1949. For a long time (from the 1950s … Read more