Zobrazeno 1 - 10
of 258
pro vyhledávání: '"Fan, Deliang"'
Autor:
Zhang, Zhaoliang, Song, Tianchen, Lee, Yongjae, Yang, Li, Peng, Cheng, Chellappa, Rama, Fan, Deliang
Recently, 3D Gaussian Splatting (3DGS) has become one of the mainstream methodologies for novel view synthesis (NVS) due to its high quality and fast rendering speed. However, as a point-based scene representation, 3DGS potentially generates a large
Externí odkaz:
http://arxiv.org/abs/2405.18784
3D Gaussian Splatting (3DGS) has made a significant stride in novel view synthesis, demonstrating top-notch rendering quality while achieving real-time rendering speed. However, the excessively large number of Gaussian primitives resulting from 3DGS'
Externí odkaz:
http://arxiv.org/abs/2405.17793
Neural radiance field (NeRF) has shown remarkable performance in generating photo-realistic novel views. Among recent NeRF related research, the approaches that involve the utilization of explicit structures like grids to manage features achieve exce
Externí odkaz:
http://arxiv.org/abs/2304.12587
Autor:
Li, Jingtao, Rakin, Adnan Siraj, Chen, Xing, Yang, Li, He, Zhezhi, Fan, Deliang, Chakrabarti, Chaitali
Federated Learning (FL) is a popular collaborative learning scheme involving multiple clients and a server. FL focuses on protecting clients' data but turns out to be highly vulnerable to Intellectual Property (IP) threats. Since FL periodically coll
Externí odkaz:
http://arxiv.org/abs/2303.08581
Inspired by the success of Self-supervised learning (SSL) in learning visual representations from unlabeled data, a few recent works have studied SSL in the context of continual learning (CL), where multiple tasks are learned sequentially, giving ris
Externí odkaz:
http://arxiv.org/abs/2303.07477
By learning a sequence of tasks continually, an agent in continual learning (CL) can improve the learning performance of both a new task and `old' tasks by leveraging the forward knowledge transfer and the backward knowledge transfer, respectively. H
Externí odkaz:
http://arxiv.org/abs/2211.00789
This work aims to tackle Model Inversion (MI) attack on Split Federated Learning (SFL). SFL is a recent distributed training scheme where multiple clients send intermediate activations (i.e., feature map), instead of raw data, to a central server. Wh
Externí odkaz:
http://arxiv.org/abs/2205.04007
Catastrophic forgetting is one of the major challenges in continual learning. To address this issue, some existing methods put restrictive constraints on the optimization space of the new task for minimizing the interference to old tasks. However, th
Externí odkaz:
http://arxiv.org/abs/2202.02931
Autor:
Tian, Wangsheng, Peng, Tianji, Fan, Xukai, Tang, Yanze, Fan, Deliang, Wang, Yifeng, Liu, Xudong, Meng, Haiyan, Gu, Long
Publikováno v:
In International Journal of Heat and Mass Transfer 15 August 2024 228
Autor:
Sun, Jingbo, Yang, Li, Zhang, Jiaxin, Liu, Frank, Halappanavar, Mahantesh, Fan, Deliang, Cao, Yu
Novelty detection aims to automatically identify out-of-distribution (OOD) data, without any prior knowledge of them. It is a critical step in data monitoring, behavior analysis and other applications, helping enable continual learning in the field.
Externí odkaz:
http://arxiv.org/abs/2112.09815