Do not have enough data? Deep learning to the rescue!
A Anaby-Tavor, B Carmeli, E Goldbraich… - Proceedings of the AAAI …, 2020 - aaai.org
Based on recent advances in natural language modeling and those in text generation
capabilities, we propose a novel data augmentation method for text classification tasks. We …
capabilities, we propose a novel data augmentation method for text classification tasks. We …
Cassava disease recognition from low‐quality images using enhanced data augmentation model and deep learning
Improvement of deep learning algorithms in smart agriculture is important to support the
early detection of plant diseases, thereby improving crop yields. Data acquisition for …
early detection of plant diseases, thereby improving crop yields. Data acquisition for …
Labels in a haystack: Approaches beyond supervised learning in biomedical applications
A Yakimovich, A Beaugnon, Y Huang, E Ozkirimli - Patterns, 2021 - cell.com
Recent advances in biomedical machine learning demonstrate great potential for data-
driven techniques in health care and biomedical research. However, this potential has thus …
driven techniques in health care and biomedical research. However, this potential has thus …
Learning data manipulation for augmentation and weighting
Manipulating data, such as weighting data examples or augmenting with new instances, has
been increasingly used to improve model training. Previous work has studied various rule-or …
been increasingly used to improve model training. Previous work has studied various rule-or …
Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection
Misinformation has become a frightening specter of society, especially fake news that
concerning Covid-19. It massively spreads on the Internet, and then induces …
concerning Covid-19. It massively spreads on the Internet, and then induces …
Use of data augmentation techniques in detection of antisocial behavior using deep learning methods
V Maslej-Krešňáková, M Sarnovský, J Jacková - Future Internet, 2022 - mdpi.com
The work presented in this paper focuses on the use of data augmentation techniques
applied in the domain of the detection of antisocial behavior. Data augmentation is a …
applied in the domain of the detection of antisocial behavior. Data augmentation is a …
Guiding generative language models for data augmentation in few-shot text classification
Data augmentation techniques are widely used for enhancing the performance of machine
learning models by tackling class imbalance issues and data sparsity. State-of-the-art …
learning models by tackling class imbalance issues and data sparsity. State-of-the-art …
Text data augmentation: Towards better detection of spear-phishing emails
M Regina, M Meyer, S Goutal - arXiv preprint arXiv:2007.02033, 2020 - arxiv.org
Text data augmentation, ie, the creation of new textual data from an existing text, is
challenging. Indeed, augmentation transformations should take into account language …
challenging. Indeed, augmentation transformations should take into account language …
Bet: A backtranslation approach for easy data augmentation in transformer-based paraphrase identification context
JP Corbeil, HA Ghadivel - arXiv preprint arXiv:2009.12452, 2020 - arxiv.org
Newly-introduced deep learning architectures, namely BERT, XLNet, RoBERTa and
ALBERT, have been proved to be robust on several NLP tasks. However, the datasets …
ALBERT, have been proved to be robust on several NLP tasks. However, the datasets …
Text augmentation using dataset reconstruction for low-resource classification
In the deployment of real-world text classification models, label scarcity is a common
problem and as the number of classes increases, this problem becomes even more …
problem and as the number of classes increases, this problem becomes even more …