Knowledge Distillation

Simplifying Transformer Structure for Lightweight CLIP Training on RTX 3090

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Simplifying Transformer Structure for Lightweight CLIP Training on RTX 3090

Contrastive Language-Image Pre-training (CLIP) has gained wide attention for its excellent zero-shot performance and outstanding transferability. However, training such large models typically requires substantial computation and storage, posing a barrier for general users with consumer-grade computers. To address this observation, this paper explores how to achieve competitive performance using only an Nvidia RTX 3090 GPU … Read more

Understanding Visual Transformers: Advantages Over CNNs

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Understanding Visual Transformers: Advantages Over CNNs

Source: Machine Heart Transformers have recently become the new dominators in the visual field. What specific applications does this model architecture from the NLP field have in the CV field? As an attention-based encoder-decoder architecture, Transformers have not only revolutionized the field of Natural Language Processing (NLP) but also made some pioneering contributions in the … Read more

Summary of Convolutional Neural Network Compression Methods

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Summary of Convolutional Neural Network Compression Methods

Click on the above “Beginner Learning Vision”, choose to add Star Mark or Top. Important content delivered at the first time For academic sharing only, does not represent the position of this public account, contact for deletion if infringing Reprinted from: Author | Tang Fen@Zhihu Source | https://zhuanlan.zhihu.com/p/359627280 Editor | Jishi Platform We know that … Read more

Fast and Effective Overview of Lightweight Transformers in Various Fields

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Fast and Effective Overview of Lightweight Transformers in Various Fields

MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and enterprise researchers. Community Vision is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for beginners. Reprinted from | RUC … Read more

Exclusive: BERT Model Compression Based on Knowledge Distillation

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Exclusive: BERT Model Compression Based on Knowledge Distillation

Authors: Siqi Sun, Yu Cheng, Zhe Gan, Jingjing Liu This article is about1800 words, recommended reading time5 minutes. This article introduces the “Patient Knowledge Distillation” model. Data Department THU backend reply“191010”, get the paper address. In the past year, there have been many groundbreaking advances in language model research, such as GPT generating sentences that … Read more

A Detailed Overview of BERT Model Compression Methods

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A Detailed Overview of BERT Model Compression Methods

Approximately 3000 words, recommended reading time 10+ minutes. This article mainly introduces methods such as <strong>knowledge distillation</strong>, <strong>parameter sharing</strong>, and <strong>parameter matrix approximation</strong>. Author | Chilia Columbia University NLP Search Recommendation Compiled by | NewBeeNLP The trend of pre-trained models based on Transformer is to become larger and larger. Although these models show significant improvements … Read more

6 Methods for Compressing Convolutional Neural Networks

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6 Methods for Compressing Convolutional Neural Networks

This articleis approximately 5200 words, recommended reading time is10+minutes We know that, to some extent, the deeper the network, the more parameters it has, and the more complex the model, the better its final performance. The compression algorithm for neural networks aims to transform a large and complex pre-trained model into a streamlined smaller model. … Read more

Distilling Llama3 into Hybrid Linear RNN with Mamba

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Distilling Llama3 into Hybrid Linear RNN with Mamba

Follow our public account to discover the beauty of CV technology This article is reprinted from Machine Heart. The key to the tremendous success of the Transformer in deep learning is the attention mechanism. The attention mechanism allows Transformer-based models to focus on parts of the input sequence that are relevant, achieving better contextual understanding. … Read more

LRC-BERT: Contrastive Learning for Knowledge Distillation

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LRC-BERT: Contrastive Learning for Knowledge Distillation

New Intelligence Report Author: Gaode Intelligent Technology Center [New Intelligence Guide]The research and development team of Gaode Intelligent Technology Center designed a contrastive learning framework for knowledge distillation in their work, and proposed COS-NCE LOSS based on this framework. This paper has been accepted by AAAI 2021. NLP (Natural Language Processing) plays an important role … Read more

BERT Model Compression Based on Knowledge Distillation

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BERT Model Compression Based on Knowledge Distillation

Big Data Digest authorized reprint from Data Pie Compiled by:Sun Siqi, Cheng Yu, Gan Zhe, Liu Jingjing In the past year, there have been many breakthrough advancements in the research of language models, such as GPT, which generates sentences that are convincingly realistic [1]; BERT, XLNet, RoBERTa [2,3,4], etc., have swept various NLP rankings as … Read more