In spite of the immense success of deep neural networks for classification tasks, it's challenging to use them to extend the usage for industrial applications. The struggle comes from the nature of variation in data sources and the distribution of data. For image classification and detection schemes, it's significant to design models that are less prone to transformation shifts. In this work, we propose LadonNet, a CNN which trains with multiple spatial transformations of the input instance. The model is extended to work with a Residual CNN model on training samples and generated augmented samples. The model was compared with other varieties of residual architectures and showed competitive performance. In the future, the model can be extended with attention to better visualize the strongest features.
Authors: Zabir Al Nazi, Mamunur Rahaman Mamun, Abidur Rahman Mallik, Tanmoy Tapos Datta, Mohammad Samawat Ullah
DOI: https://doi.org/10.1145/3377049.3377092
Publish Year: 2020
