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Unknown Class Label Cleaning For Learning With Open-Set Noisy Labels
2020 IEEE International Conference on Image Processing (ICIP), 2020Deep neural networks (DNNs) trained on large-scale annotated datasets have achieved impressive results in the area of image classification. Many large-scale datasets have been collected from websites; however, such data are inevitably corrupted with noise.
Qing Yu 0013, Kiyoharu Aizawa
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Targeted Clean-Label Poisoning Attacks on Federated Learning
2023Federated Learning (FL) has become one of the most extensively utilized distributed training approaches since it allows users to access large datasets without really sharing them. Only the updated model parameters are exchanged with the central server after the model has been trained locally on the devices holding the data.
Ayushi Patel, Priyanka Singh 0001
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Consumer perception of clean food labels
British Food Journal, 2022PurposeThis study puts forth a consumer-oriented concept of clean labels and attempts to empirically investigate consumer perceptions of these labels.Design/methodology/approachA self-administered survey (n = 346) was used as the research instrument for data collection in the current study.FindingsResults from an online survey indicate that consumers ...
Yan Cao, Li Miao
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Cleaning It Up—What Is a Clean Label Ingredient?
Cereal Foods World, 2015In this column, Busken looks at the process by which ingredients are made and uses this as a basis for determining whether they are “clean label” or not. One of the challenges in making this determination is presented by the degree to which even the simpler processes are performed and how they are dealt with in labeling laws.
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Euclidean distance based label noise cleaning
2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), 2017Quality of the datasets play an important role in performance of supervised classifiers. In presence of mislabelled examples, the performance of such classifiers degrades severely. In this paper we propose a label noise cleaning approach based on euclidean distance.
Muhammad Ammar Malik, Moonsoo Kang
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SplitNet: Learnable Clean-Noisy Label Splitting for Learning With Noisy Labels
International Journal of Computer VisionAbstract Annotating the dataset with high-quality labels is crucial for deep networks’ performance, but in real-world scenarios, the labels are often contaminated by noise. To address this, some methods were recently proposed to automatically split clean and noisy labels among training data, and learn a semi-supervised learner in a ...
Seungryong Kim +2 more
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Automatic cleaning and labelling process for electrogastrogram
2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), 2018It is well known that recording electrical signals from the human body is a challenging task due to their nature and its vulnerability to noise effects. This is also the case of the electrogastrographic signal, the electrical activity of the digestive system that can be recorded using surface electrodes on the external wall of the abdomen.
Karina I. Espinosa-Espejel +3 more
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Functional ice cream with a "clean label"
2020High market competitiveness as well as in creased interest in health-related products forces producers to create new products and innovative production technologies that would encourage a potential customer to buy. The idea of "clean label" enjoys growing popularity due to the strong in terest in healthy, unprocessed products and simple ingredients ...
Motyl, Wojciech +4 more
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Cultural and moral dimensions of “clean food” labeling
International Journal of Eating Disorders, 2021James I. Hudson, Kristin N. Javaras
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