segnet权重

Abstract Most image completion methods produce only one result for each masked input, although there may be many reasonable possibilities. In this paper, we present an approach for pluralistic image completion – the task of generating multiple and diverse plausible solutions for image completion. A major challenge faced by learning-based approaches is that usually only one ground truth training instance per label. As such, sampling from conditional VAEs still leads to minimal diversity. To overcome this, we propose a novel and probabilistically principled framework with two parallel paths. One is a reconstructive path that utilizes the only one given ground truth to get prior distribution of missing parts and rebuild the original image from this distribution. The other is a generative path for which the conditional prior is coupled to the distribution obtained in the reconstructive path. Both are supported by GANs. We also introduce a new short+long term attention layer that exploits distant relations among decoder and encoder features, improving appearance consistency. When tested on datasets with buildings (Paris), faces (CelebA-HQ), and natural images (ImageNet), our method not only generated higherquality completion results, but also with multiple and diverse plausible outputs.

标注好的打电话数据集，包括图片和标注好的xml文件，里面包含图片和xml文件，可用于yolov4训练、yolov5训练，训练可达到百分90以上。包含图片和使用labelimg软件标注生成的.xml文件，已全部手工标注完成，可以训练完整的识别度极高的.h5模型和.pth模型

烟雾数据集，手工精细标注，里面包含图片和xml文件，可用于yolov4训练、yolov5训练，训练可达到百分90以上。包含图片和使用labelimg软件标注生成的.xml文件，已全部手工标注完成，可以训练完整的识别度极高的.h5模型和.pth模型

基于RBF神经网络的曲线拟合仿真,使用matlab2021a或者更高版本测试

模糊神经网络数据预测仿真,使用matlab2021a或者更高版本测试

Deep AI 吴恩达深度学习课后检验题-03.结构化机器学习项目

ESN神经网络。一种基于ESN神经网络的移动机器人场景识别方法：从构成场景的图像序列中，提取ORB特征点，再从提取到的ORB特征点中，matlab2021a测试。

BP神经网络拟合预测，适合初学者

现在搞AI研究写论文，其中论文里的框架图模型图很是考验你画图的能力，不费一番心思功夫，怎能画出一个入得Reviewer法眼的图，论文也不大好中，很是发愁。好消息来了，elvis介绍了有个ML visual的利器，提供一份103页的PPT模型图素材，你在上面可以直接使用画出你要的机器学习深度学习模型图，再也不用担心画图了！ URL：https://github.com/dair-ai/ml-visuals https://mp.weixin.qq.com/s/QoMY4NLUmdTyLkWx8zD_hA