Zobrazeno 1 - 10
of 23
pro vyhledávání: '"OMAR, BOURAHLA"'
As an important and challenging problem, multi-domain learning (MDL) typically seeks for a set of effective lightweight domain-specific adapter modules plugged into a common domain-agnostic network. Usually, existing ways of adapter plugging and stru
Externí odkaz:
http://arxiv.org/abs/2007.12415
In this paper, we propose a novel learning scheme called epoch-evolving Gaussian Process Guided Learning (GPGL), which aims at characterizing the correlation information between the batch-level distribution and the global data distribution. Such corr
Externí odkaz:
http://arxiv.org/abs/2006.14347
The Generative Models have gained considerable attention in the field of unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to its outstanding data generation capability. Many models of GAN have p
Externí odkaz:
http://arxiv.org/abs/2006.05132
Knowledge distillation, aimed at transferring the knowledge from a heavy teacher network to a lightweight student network, has emerged as a promising technique for compressing neural networks. However, due to the capacity gap between the heavy teache
Externí odkaz:
http://arxiv.org/abs/2006.04719
Autor:
Chen, Yifeng, Lin, Guangchen, Li, Songyuan, Omar, Bourahla, Wu, Yiming, Wang, Fangfang, Feng, Junyi, Xu, Mingliang, Li, Xi
Panoptic segmentation aims to perform instance segmentation for foreground instances and semantic segmentation for background stuff simultaneously. The typical top-down pipeline concentrates on two key issues: 1) how to effectively model the intrinsi
Externí odkaz:
http://arxiv.org/abs/2003.14031
Publikováno v:
ACM Transactions on Multimedia Computing, Communications, and Applications. 19:1-41
Deep Learning has implemented a wide range of applications and has become increasingly popular in recent years. The goal of multimodal deep learning (MMDL) is to create models that can process and link information using various modalities. Despite th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cfbff7afc7d2dff8893761be415c9b51
https://doi.org/10.1145/3545572
https://doi.org/10.1145/3545572
Akademický článek
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Publikováno v:
ACM Computing Surveys; Nov2022, Vol. 54 Issue 8, p1-49, 49p
Akademický článek
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Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems; November 2022, Vol. 33 Issue: 11 p6532-6544, 13p