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Improved Denoising Diffusion Probabilistic Models (DDPM) and Its Application in Image Generation
2021年OpenAI发表的论文《Improved Denoising Diffusion Probabilistic Models》对DDPM算法进行了改进,采用余弦函数的噪声Schedule,代码开源且使用PyTorch框架。本文介绍了其训练和采样脚本,并讨论了改进算法相对于原始DDPM的优势。- 372
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Bayesian Image Super-Resolution with Deep Modeling of Image Statistics
这篇论文介绍了基于贝叶斯理论的图像超分辨率网络(BayeSR),结合平滑性和稀疏性先验对自然图像进行统计建模,提出了对图像先验建模的贝叶斯图像恢复框架。- 210
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Stable Diffusion v1-5模型权重下载及使用教程
学习如何下载和使用Stable Diffusion v1-5模型权重,包括环境配置、权重下载、txt2img使用、解决bug等内容。本教程详细介绍了txt2img的超参数,以及如何修复bug。- 118
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GhostNet: A Lightweight Neural Network Architecture for Image Classification
介绍了GhostNet轻量级神经网络架构的原理、优点、性能和实现,以及其在ImageNet数据集上的表现。- 259
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Stable Diffusion: A Deep Learning Model Explained
Learn about Stable Diffusion, a deep learning model that explains text-to-image generation and reverse diffusion process.- 79
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Triton开发实践:使用PyTorch和Triton进行图片分类
本文分享了在Triton中使用PyTorch进行图片分类的开发实践,包括环境准备、模型准备和转换,以及使用tritonclient库进行推理服务。- 321
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Positional Encoding in Transformer: Understanding and Methods
Explore the concept of positional encoding in transformer models and different methods of constructing positional encoding. Learn about the importance of positional encoding in self-attention and how …- 241
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The Latest Developments in AI Technology: A Comprehensive Overview
Explore the most recent advancements in AI technology, including machine learning and deep learning, and stay updated on the latest trends in artificial intelligence.- 134
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U-Net: Convolutional Networks for Biomedical Image Segmentation
了解U-Net模型的结构及其在生物医学图像分割中的应用。该模型采用了编码器-解码器结构,并使用残差连接来改善梯度流,通过downsampling和upsampling实现特征提取和精确定位。文章介绍了U-Net的基本原理和图像处理过程。- 241
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M-SQL: Multi-Task Representation Learning for Single-Table Text2sql Generation
了解如何利用M-SQL基于ModelArts实现Text2SQL的多任务表示学习,使用PyTorch和预训练的BERT模型进行文本到SQL的生成。- 182
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TVM VS TensorRT: Performance Comparison with ResNet18 Model
This article compares the performance of TVM and TensorRT with the ResNet18 model, providing insights into model optimization and inference speed.- 490
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Multi-scale Deep Learning Architectures for Person Re-identifification
This article introduces the role of multi-scale image resolution in person re-identification tasks and the idea of parameter sharing or feature interaction at corresponding layers, with a focus on the…- 286
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Grounded Segment Anything: Revolutionizing Computer Vision with SAM and Stable Diffusion
Discover how the Grounded Segment Anything project is revolutionizing computer vision with the SAM and Stable Diffusion models. Learn about the latest advancements in AI technology and image processin…- 451
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Squeeze-and-Excitation Networks: Improving Convolutional Neural Networks with Channel Relationships
Squeeze-and-Excitation Networks (SENets) are a novel architecture unit proposed to improve the representation capability of Convolutional Neural Networks by explicitly modeling the interdependence of …- 112
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Dropout Reduces Underfitting: Exploring the Early Dropout Approach for Training Neural Networks
本文探讨了如何使用Dropout来缓解神经网络的欠拟合问题,通过早期使用Dropout来训练神经网络,作者发现可以减小欠拟合现象,从而提高模型的训练效果。- 68
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GNN中的Positional Encoding和超大图压缩技术研究
本文研究了在GNN中应用Positional Encoding以及一种高效的超大图压缩技术,实验结果表明这些方法能够显著提升性能并实现高效的图压缩。- 151
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Going Deeper with Image Transformers: Exploring the Potential of Transformer Models in Image Processing
This article explores the potential of using Transformer models in image processing, drawing inspiration from the concept of residual connections in deep neural networks. It discusses the core contrib…- 151
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Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
学习如何使用Latent Consistency Models来合成高分辨率图像,并进行少步推理的方法。- 422
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深度解析Prompt Learning:从Prompt Engineering到Training strategy
本文深度解析了Prompt Learning的背景、定义和三个部分,包括Prompt Engineering、Answer engineering、Multi-prompt learning、Training strategy,旨在帮助读者全面了解这一重要概念。- 174
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OtterHD-8B: 80 Billion Parameter Multimodal Model for High-Resolution Input
OtterHD-8B is an 80 billion parameter multimodal model capable of handling high-resolution inputs, developed by a Chinese team from Nanyang Technological University based on Fuyu-8B. It outperforms si…- 629
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ChatGPT Prompt Engineering for Developers: 5 Essential Articles
这是《ChatGPT Prompt Engineering for Developers》课程的5篇关键文章合集,包括编写Prompt的关键原则、文本总结、文本推理、文本转换以及邮件回复与营销文案。课程由吴恩达和OpenAI合作开发,免费提供,内容简单易懂,含金量非常高。- 316
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