Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Ghiassi, Amirmasoud"'
Autor:
Pene, Cosmin Octavian, Ghiassi, Amirmasoud, Younesian, Taraneh, Birke, Robert, Chen, Lydia Y.
Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels. Not only acquiring a clean and fully labeled dataset in multi-label learning is extremely expensive, but also many of the actual
Externí odkaz:
http://arxiv.org/abs/2108.02032
Labeling real-world datasets is time consuming but indispensable for supervised machine learning models. A common solution is to distribute the labeling task across a large number of non-expert workers via crowd-sourcing. Due to the varying backgroun
Externí odkaz:
http://arxiv.org/abs/2011.06833
Robustness to label noise is a critical property for weakly-supervised classifiers trained on massive datasets. Robustness to label noise is a critical property for weakly-supervised classifiers trained on massive datasets. In this paper, we first de
Externí odkaz:
http://arxiv.org/abs/2007.06324
Today's available datasets in the wild, e.g., from social media and open platforms, present tremendous opportunities and challenges for deep learning, as there is a significant portion of tagged images, but often with noisy, i.e. erroneous, labels. R
Externí odkaz:
http://arxiv.org/abs/2007.05305
Autor:
Yousefi, Mostafa Hadadian Nejad, Ghiassi, Amirmasoud, Hashemi, Boshra Sadat, Goudarzi, Maziar
Smart devices have become an indispensable part of our lives and gain increasing applicability in almost every area. Latency-aware applications such as Augmented Reality (AR), autonomous driving, and online gaming demand more resources such as networ
Externí odkaz:
http://arxiv.org/abs/2003.02820
Noisy labeled data is more a norm than a rarity for self-generated content that is continuously published on the web and social media. Due to privacy concerns and governmental regulations, such a data stream can only be stored and used for learning p
Externí odkaz:
http://arxiv.org/abs/2001.10399
Noisy labeled data is more a norm than a rarity for crowd sourced contents. It is effective to distill noise and infer correct labels through aggregation results from crowd workers. To ensure the time relevance and overcome slow responses of workers,
Externí odkaz:
http://arxiv.org/abs/1807.07291