一个很好的网站，可以直接看到最新的arXiv文章: Transfer learning, Domain adaptation. 生成对抗U-Net：实现Domain-free医学图像增广. ★ Research and Development of Cutting-Edge Machine Learning/Deep Learning Algorithms including (but not limited to) Few-Shot learning, Meta-learning, Quantizations and Domain Adaptation using PyTorch, MXNet ★ Implementing and Verifying the Reproducibility of some interesting Cutting-Edge Machine Learning/Deep Learning Research… 重新标记ImageNet：从单标签到多标签，从全局标签到局部标签. 郑哲东：【新无人机数据集】从 行人重识别 到 无人机目标定位. 07/2018: PyTorch version for our CVPR18RDN has been implemented by Nguyễn Trần Toàn (trantoan060689@gmail.com) and merged into EDSR-PyTorch. 域适应已经是一个很火的方向了，目标检测更不用说，二者结合的工作也开始出现了，这里我总结了cvpr18和cvpr19的相关论文，希望对这个交叉方向的近况有一个了解。 1. On the contrary, Manual Testing is performed by a human sitting in front of a computer carefully executing the test steps. Perhaps my most well-known works are on visual tracking, but I have many favourite topics: robot mapping, meta-learning, continual learning, self-supervised learning and optimisation.. My talented DPhil students: 04/2019: We release all the train/test codes and pre-trained models for ICLR19RNAN at RNAN. 12/2018: We have 1 paper accepted to ICLR 2019. 在源域上训练，直接迁移到目标域上，要求目标域也要有尽可能好的表现。本质上就是将在两种不同分布的数据集之间寻找一种“迁移”。 Gradient Reversal Layer. 全能涨点！TDAF：用于视觉任务的自上而下的注意力框架 | AAAI 2021. 京东AI发布FaceX-Zoo：用于人脸识别的PyTorch工具箱 Domain Adaptation 1 2 6/11: HW13: Meta Learning - MAML, Meta Learning - Gradient Descent and Metric-based (option) Meta 1 2: slide, video 1 2 3 (TA:姜成翰、高瑋聰) More about Meta 1 2 7/02: HW14: Life-long Learning Life-long: slide, video (TA:紀伯翰、黃子賢) More about Life-long 7/02: HW15 京东AI发布FaceX-Zoo：用于人脸识别的PyTorch工具箱 最后感谢大家看完～欢迎关注分享点赞～也可以check我的一些其他文章. A good website to see the latest arXiv preprints by search: Transfer learning, Domain adaptation. Optimizing PyTorch training code. Optimizing PyTorch training code. 梯度下降是最小化目标函数，向负的梯度方向优化就是最大化目标函数。 Automation Testing or Test Automation is a software testing technique that performs using special automated testing software tools to execute a test case suite. 梯度下降是最小化目标函数，向负的梯度方向优化就是最大化目标函数。 Domain adaptation transforms (augmentations.domain_adaptation) Functional transforms (augmentations.functional) ... After that, we will apply ToTensorV2 that converts a NumPy array to a PyTorch tensor, which will serve as an input to a neural network. Domain Adaptation. 在使用PyTorch做实验时经常会用到生成随机数Tensor的方法，比如：torch.rand()torch.randn()torch.normal()torch.linespace()在很长一段时间里我都没有区分这些方法生成的随机数究竟有什么不同，由此在做实验的时候经常会引起一些莫名其妙的麻烦。所以在此做一个总结，以供大家阅读区分，不要重蹈我的覆辙。 域适应已经是一个很火的方向了，目标检测更不用说，二者结合的工作也开始出现了，这里我总结了cvpr18和cvpr19的相关论文，希望对这个交叉方向的近况有一个了解。 1. 迁移学习文章汇总 Awesome transfer learning papers. The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits. CyCADA: Cycle-Consistent Adversarial Domain Adaptation Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, Trevor Darrell ICML 2018 paper | code: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Jun-Yan Zhu*, Taesung Park*, Phillip Isola, and Alexei A. Efros 重新标记ImageNet：从单标签到多标签，从全局标签到局部标签. A good website to see the latest arXiv preprints by search: Transfer learning, Domain adaptation. 迁移学习文章汇总 Awesome transfer learning papers. What is Automation Testing? A segmentation model trained on the Cityscapes-style GTA images yields mIoU of 37.0 on the segmentation task on Cityscapes. 04/2019: We release all the train/test codes and pre-trained models for ICLR19RNAN at RNAN. What: 本文研究的是领域迁移问题中错误的伪标签(Pseudo Label)的问题，探讨了如何自动设定阈值来修正这种伪标签学习。; 之前的伪标签往往通过人为卡阈值(Threshold) 的方式来学高置信度的伪标签(Pseudo Label)，而忽略低置信度的标签。 但是这个阈值(Threshold) 往往很难决定。 2018_cvpr 全能涨点！TDAF：用于视觉任务的自上而下的注意力框架 | AAAI 2021. 生成对抗U-Net：实现Domain-free医学图像增广. support both image- and video-reid. The PyTorch code for MST is on the way. 2018_cvpr CyCADA: Cycle-Consistent Adversarial Domain Adaptation Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, Trevor Darrell ICML 2018 paper | code: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks Jun-Yan Zhu*, Taesung Park*, Phillip Isola, and Alexei A. Efros On the contrary, Manual Testing is performed by a human sitting in front of a computer carefully executing the test steps. This is a pytorch implementation of the paper Unsupervised Domain Adaptation by Backpropagation Environment. Pytorch 1.0; Python 2.7; Network Structure. PyTorch and Albumentations for image classification ... Domain adaptation transforms (augmentations.domain_adaptation) Functional transforms (augmentations.functional) Helper functions for working with bounding boxes (augmentations.bbox_utils) 郑哲东：利用Uncertainty修正Domain Adaptation中的伪标签. It has a training set of 60,000 examples, and a test set of 10,000 examples. Hi, my name is João F. Henriques. Pytorch 1.0; Python 2.7; Network Structure. It features: multi-GPU training. The GTA → Cityscapes results of CycleGAN can be used for domain adaptation for segmentation. Perhaps my most well-known works are on visual tracking, but I have many favourite topics: robot mapping, meta-learning, continual learning, self-supervised learning and optimisation.. My talented DPhil students: Torchreid is a library for deep-learning person re-identification, written in PyTorch. 郑哲东：利用Uncertainty修正Domain Adaptation中的伪标签. ★ Research and Development of Cutting-Edge Machine Learning/Deep Learning Algorithms including (but not limited to) Few-Shot learning, Meta-learning, Quantizations and Domain Adaptation using PyTorch, MXNet ★ Implementing and Verifying the Reproducibility of some interesting Cutting-Edge Machine Learning/Deep Learning Research… First, you need download the target dataset mnist_m from pan.baidu.com fetch code: kjan or Google Drive incredibly easy preparation of reid datasets. I like to work in the convex hull of machine learning, deep learning and computer vision. Torchreid is a library for deep-learning person re-identification, written in PyTorch. support both image- and video-reid. 最后感谢大家看完～欢迎关注分享点赞～也可以check我的一些其他文章. First, you need download the target dataset mnist_m from pan.baidu.com fetch code: kjan or Google Drive Hi, my name is João F. Henriques. Domain adaptation transforms (augmentations.domain_adaptation) Functional transforms (augmentations.functional) ... After that, we will apply ToTensorV2 that converts a NumPy array to a PyTorch tensor, which will serve as an input to a neural network. This is a pytorch implementation of the paper Unsupervised Domain Adaptation by Backpropagation Environment. 郑哲东：【新无人机数据集】从 行人重识别 到 无人机目标定位. What is Automation Testing? M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning [arXiv 6 Jul 2018] [Pytorch(official)] Factorized Adversarial Networks for Unsupervised Domain Adaptation [arXiv 4 Jun 2018] DiDA: Disentangled Synthesis for Domain Adaptation [arXiv 21 May 2018] 郑哲东：用CNN分100,000类图像 Domain Adaptation 1 2 6/11: HW13: Meta Learning - MAML, Meta Learning - Gradient Descent and Metric-based (option) Meta 1 2: slide, video 1 2 3 (TA:姜成翰、高瑋聰) More about Meta 1 2 7/02: HW14: Life-long Learning Life-long: slide, video (TA:紀伯翰、黃子賢) More about Life-long 7/02: HW15 end-to-end training and evaluation. It has a training set of 60,000 examples, and a test set of 10,000 examples. The PyTorch code for MST is on the way. 80GB 医学影像数据集发布！OCTA-500公开下载. Dataset. Latest papers. 07/2018: PyTorch version for our CVPR18RDN has been implemented by Nguyễn Trần Toàn (trantoan060689@gmail.com) and merged into EDSR-PyTorch. This feature is useful for domain adaptation research. 80GB 医学影像数据集发布！OCTA-500公开下载. 一个很好的网站，可以直接看到最新的arXiv文章: Transfer learning, Domain adaptation. What: 本文研究的是领域迁移问题中错误的伪标签(Pseudo Label)的问题，探讨了如何自动设定阈值来修正这种伪标签学习。; 之前的伪标签往往通过人为卡阈值(Threshold) 的方式来学高置信度的伪标签(Pseudo Label)，而忽略低置信度的标签。 但是这个阈值(Threshold) 往往很难决定。 end-to-end training and evaluation. incredibly easy preparation of reid datasets. A segmentation model trained on the Cityscapes-style GTA images yields mIoU of 37.0 on the segmentation task on Cityscapes. Dataset. M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning [arXiv 6 Jul 2018] [Pytorch(official)] Factorized Adversarial Networks for Unsupervised Domain Adaptation [arXiv 4 Jun 2018] DiDA: Disentangled Synthesis for Domain Adaptation [arXiv 21 May 2018] I like to work in the convex hull of machine learning, deep learning and computer vision. 郑哲东：用CNN分100,000类图像 This feature is useful for domain adaptation research. The GTA → Cityscapes results of CycleGAN can be used for domain adaptation for segmentation. Domain Adaptation. 在源域上训练，直接迁移到目标域上，要求目标域也要有尽可能好的表现。本质上就是将在两种不同分布的数据集之间寻找一种“迁移”。 Gradient Reversal Layer. The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits. 12/2018: We have 1 paper accepted to ICLR 2019. Automation Testing or Test Automation is a software testing technique that performs using special automated testing software tools to execute a test case suite. 在使用PyTorch做实验时经常会用到生成随机数Tensor的方法，比如：torch.rand()torch.randn()torch.normal()torch.linespace()在很长一段时间里我都没有区分这些方法生成的随机数究竟有什么不同，由此在做实验的时候经常会引起一些莫名其妙的麻烦。所以在此做一个总结，以供大家阅读区分，不要重蹈我的覆辙。 It features: multi-GPU training. Latest papers.
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