Browsing by Author "Sahito, Attaullah"
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A study of self-training variants for semi-supervised image classification
Sahito, Attaullah (The University of Waikato, 2021)Artificial neural networks achieve state-of-the-art performance when trained on a vast number of labelled examples. Still, they can easily overfit training examples when few labelled examples are available. The requirement ... -
Semi-supervised learning using Siamese networks
Sahito, Attaullah; Frank, Eibe; Pfahringer, Bernhard (Springer, 2019)Neural networks have been successfully used as classification models yielding state-of-the-art results when trained on a large number of labeled samples. These models, however, are more difficult to train successfully for ... -
Transfer of pretrained model weights substantially improves semi-supervised image classification
Sahito, Attaullah; Frank, Eibe; Pfahringer, Bernhard (Springer, 2020)Deep neural networks produce state-of-the-art results when trained on a large number of labeled examples but tend to overfit when small amounts of labeled examples are used for training. Creating a large number of labeled ...
Co-authors for Attaullah Sahito
Attaullah Sahito has 2 co-authors in Research Commons.