Unsupervised Learning of Visual Features for Fashion Classification
Abstract: Deep Learning has changed the way computer vision tasks are being solved in the current age. Deep Learning approaches have achieved state-of-the-art results in computer vision problems like image classification, image verification, object detection, and image segmentation. However, most of this success has been achieved by training deep neural networks on labelled datasets. While this way of training the neural networks results in classifiers with better accuracies, but it might not be the most efficient way to solve computer vision problems. This is so because it is a resource consuming process to manually label the images/data-points and can cost a lot of time and money to the organizations that employ deep learning for developing various products and services.Fashion and e-commerce is one such domain where there is a need to leverage the image data without relying too much on labels. This process can be beneficial to automatically label the category, attributes and other metadata of images, generally used to show the inventory digitally, without relying on humans to manually annotate them.The aim of this master thesis is to explore the effectiveness of unsupervised deep learning approaches for fashion classification so that the data can be classified by only relying on a few labelled data points. Two unsupervised approaches, one based on clustering of features called DeepCluster and the other based on rotation as a self-supervision task, are compared to a fully supervised model on DeepFashion dataset.Through empirical experiments, it has been shown that these unsupervised deep learning techniques can be used to attain comparable classification accuracies (~1-4 % lesser than that achieved by a fully supervised model) and thus making them as suitable alternatives to supervised approaches.
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