Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Di, Zonglin"'
Autor:
Liu, Minghao, Di, Zonglin, Wei, Jiaheng, Wang, Zhongruo, Zhang, Hengxiang, Xiao, Ruixuan, Wang, Haoyu, Pang, Jinlong, Chen, Hao, Shah, Ankit, Wei, Hongxin, He, Xinlei, Zhao, Zhaowei, Wang, Haobo, Feng, Lei, Wang, Jindong, Davis, James, Liu, Yang
Large-scale data collection is essential for developing personalized training data, mitigating the shortage of training data, and fine-tuning specialized models. However, creating high-quality datasets quickly and accurately remains a challenge due t
Externí odkaz:
http://arxiv.org/abs/2408.11338
Autor:
Di, Zonglin, Zhu, Zhaowei, Jia, Jinghan, Liu, Jiancheng, Takhirov, Zafar, Jiang, Bo, Yao, Yuanshun, Liu, Sijia, Liu, Yang
The objective of machine unlearning (MU) is to eliminate previously learned data from a model. However, it is challenging to strike a balance between computation cost and performance when using existing MU techniques. Taking inspiration from the infl
Externí odkaz:
http://arxiv.org/abs/2406.07698
This paper focuses on the challenge of machine unlearning, aiming to remove the influence of specific training data on machine learning models. Traditionally, the development of unlearning algorithms runs parallel with that of membership inference at
Externí odkaz:
http://arxiv.org/abs/2406.07687
Automatic road extraction from satellite imagery using deep learning is a viable alternative to traditional manual mapping. Therefore it has received considerable attention recently. However, most of the existing methods are supervised and require pi
Externí odkaz:
http://arxiv.org/abs/2309.07823
Warning: This paper contains several contents that may be toxic, harmful, or offensive. In the last few years, text-to-image generative models have gained remarkable success in generating images with unprecedented quality accompanied by a breakthroug
Externí odkaz:
http://arxiv.org/abs/2306.00905
Federated embodied agent learning protects the data privacy of individual visual environments by keeping data locally at each client (the individual environment) during training. However, since the local data is inaccessible to the server under feder
Externí odkaz:
http://arxiv.org/abs/2211.14769
Autor:
Zheng, Kaizhi, Zhou, Kaiwen, Gu, Jing, Fan, Yue, Wang, Jialu, Di, Zonglin, He, Xuehai, Wang, Xin Eric
Building a conversational embodied agent to execute real-life tasks has been a long-standing yet quite challenging research goal, as it requires effective human-agent communication, multi-modal understanding, long-range sequential decision making, et
Externí odkaz:
http://arxiv.org/abs/2208.13266
We propose to personalize a human pose estimator given a set of test images of a person without using any manual annotations. While there is a significant advancement in human pose estimation, it is still very challenging for a model to generalize to
Externí odkaz:
http://arxiv.org/abs/2107.02133
Deep learning is revolutionizing the mapping industry. Under lightweight human curation, computer has generated almost half of the roads in Thailand on OpenStreetMap (OSM) using high-resolution aerial imagery. Bing maps are displaying 125 million com
Externí odkaz:
http://arxiv.org/abs/1905.01447
Publikováno v:
In Addictive Behaviors Reports December 2019 10
