1 code implementation • NeurIPS 2023 • Shenzhi Wang, Qisen Yang, Jiawei Gao, Matthieu Gaetan Lin, Hao Chen, Liwei Wu, Ning Jia, Shiji Song, Gao Huang
Existing solutions tackle this problem by imposing a policy constraint on the policy improvement objective in both offline and online learning.
no code implementations • 16 Jul 2020 • Philip T. Jackson, Stephen Bonner, Ning Jia, Christopher Holder, Jon Stonehouse, Boguslaw Obara
We show that correlations between the camera used to acquire an image and the class label of that image can be exploited by convolutional neural networks (CNN), resulting in a model that "cheats" at an image classification task by recognizing which camera took the image and inferring the class label from the camera.
no code implementations • 15 Jun 2020 • Xin Zhang, Ning Jia, Ioannis Ivrissimtzis
Our results show that the effect of the illumination model is important, comparable in significance to the network architecture.
no code implementations • 23 May 2019 • Xin Zhang, Ning Jia, Ioannis Ivrissimtzis
We conclude that in our application domain of information retrieval from 3D printed objects, where access to the exact CAD files of the printed objects can be assumed, one can use inexpensive synthetic data to enhance neural network training, reducing the need for the labour intensive process of creating large amounts of hand labelled real data or the need to generate photorealistic synthetic data.
1 code implementation • 20 Nov 2018 • Qian Wang, Ning Jia, Toby P. Breckon
Recent studies on multi-label image classification have focused on designing more complex architectures of deep neural networks such as the use of attention mechanisms and region proposal networks.