一. 概述 论文地址:链接 代码地址:链接 论文简介: 此篇论文是在CGNet上增加部分限制loss而来 核心部分是将gt框变为mask进行蒸馏 注释:仅为阅读论文和代码,未进行试验,如有漏错请不吝指出.文章的疑惑和假设仅代表个人想法. 二. 详细 2.1 Focal Distillation 2.1.1 mask计算 此篇文章在目标检测蒸馏中对FPN层进行限制,正常的操作如下公式(1)所示: \[L_{f e a}=\frac{1}{C H W} \sum_{k=1}^{C} \sum_{i=…

Awesome Knowledge Distillation 2018-07-19 10:38:40  Reference:https://github.com/dkozlov/awesome-knowledge-distillation Papers Combining labeled and unlabeled data with co-training, A. Blum, T. Mitchell, 1998 Model Compression, Rich Caruana, 2006 Dar…

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一.解决问题 如何将特征融合与知识蒸馏结合起来,提高模型性能 二.创新点 支持多子网络分支的在线互学习 子网络可以是相同结构也可以是不同结构 应用特征拼接.depthwise+pointwise,将特征融合和知识蒸馏结合起来 三.实验方法和理论 1.Motivation DML (Deep Mutual Learning) 算法思想: ​ 用两个子网络(可以是不同的网络结构)进行在线互学习,得到比单独训练性能更好的网络 损失函数: ​ 传统监督损失函数: ​ 模仿性的损失函数: ​ 单个网络的损…

IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017. IEEE Computer Society 2017, ISBN 978-1-5386-1032-9 Oral Session 1 Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Corre…

CVPR2017 paper list Machine Learning 1 Spotlight 1-1A Exclusivity-Consistency Regularized Multi-View Subspace Clustering Xiaojie Guo, Xiaobo Wang, Zhen Lei, Changqing Zhang, Stan Z. Li Borrowing Treasures From the Wealthy: Deep Transfer Learning Thro…

基于COCO数据集验证的目标检测算法天梯排行榜 AP50 Rank Model box AP AP50 Paper Code Result Year Tags 1 SwinV2-G (HTC++) 63.1 Swin Transformer V2: Scaling Up Capacity and Resolution Link 2021 Swin-Transformer 2 Florence-CoSwin-H 62.4 Florence: A New Foundation Model for C…

The Brain as a Universal Learning Machine This article presents an emerging architectural hypothesis of the brain as a biological implementation of a Universal Learning Machine.  I present a rough but complete architectural view of how the brain work…

Classifying plankton with deep neural networks The National Data Science Bowl, a data science competition where the goal was to classify images of plankton, has just ended. I participated with six other members of my research lab, the Reservoir lab o…

https://blog.csdn.net/y80gDg1/article/details/81463731 感谢阅读腾讯AI Lab微信号第34篇文章.当地时间 7 月 10-15 日,第 35 届国际机器学习会议(ICML 2018)在瑞典斯德哥尔摩成功举办.ICML 2018 所接收的论文的研究主题非常多样,涵盖深度学习模型/架构/理论.强化学习.优化方法.在线学习.生成模型.迁移学习与多任务学习.隐私与安全等,在本文中,腾讯 AI Lab 的研究者结合自身的研究重心和研究兴趣对部分 IC…