Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent

no code implementations • 11 Feb 2024 • Liu Ziyin, Mingze Wang, Hongchao Li, Lei Wu

Symmetries are prevalent in deep learning and can significantly influence the learning dynamics of neural networks.

Day-Night Cross-domain Vehicle Re-identification

1 code implementation • CVPR 2024 • Hongchao Li, Jingong Chen, Aihua Zheng, Yong Wu, Yonglong Luo

In this work we address the Day-Night cross-domain ReID (DN-ReID) problem and provide a new cross-domain dataset named DN-Wild including day and night images of 2286 identities giving in total 85945 daytime images and 54952 nighttime images.

Vehicle Re-Identification

Law of Balance and Stationary Distribution of Stochastic Gradient Descent

no code implementations • 13 Aug 2023 • Liu Ziyin, Hongchao Li, Masahito Ueda

The stochastic gradient descent (SGD) algorithm is the algorithm we use to train neural networks.

Attributes Guided Feature Learning for Vehicle Re-identification

no code implementations • 22 May 2019 • Hongchao Li, Xianmin Lin, Aihua Zheng, Chenglong Li, Bin Luo, Ran He, Amir Hussain

In particular, our network is end-to-end trained and contains three subnetworks of deep features embedded by the corresponding attributes (i. e., camera view, vehicle type and vehicle color).

Generative Adversarial Network Vehicle Re-Identification

The Liver Tumor Segmentation Benchmark (LiTS)

6 code implementations • 13 Jan 2019 • Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.

Benchmarking Computed Tomography (CT) +3