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dc.contributor.authorLee, Tian Faen_US
dc.description.abstractImage classification is a task used to identify what each individual image represents. Multiclass classification is a type of classification task which separates data into more than two classes. CNN, a class of deep neural network, is commonly used to analyse visual images. However, insufficient data will lead to lower accuracy in image classification. To elevate the performance of CNN, it is necessary to increase the amount of quality training data. Research have been done in DCGAN, but they did not evaluate whether different types of synthetic data are useful for data augmentation of images. In this project, a CNN model is proposed to evaluate the accuracy of different types of synthetic data and investigate their usefulness in multiclass classification. Due to limitation in computational power and memory, datasets with 10 classes such as MNIST and CIFAR-10 are used in the experiment. Different sets of tools such as DCGAN and pre-trained deep learning models and computer vision algorithms are used to generate DCGAN-original, cartoonized and sketched version of the dataset respectively. They are then added to the training dataset of the CNN with its test accuracy evaluated. After analysing the results, DCGAN-original data has the highest performance as its test accuracy is highest among the three. Furthermore, there is a higher potential to improve the accuracy of the DCGAN-original dataset by fine-tuning the DCGAN.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleSynthesizing data for multiclass image classificationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorAlex Chichung Koten_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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