Robust Deep Unsupervised Learning Framework to Discover Unseen Plankton Species
Journal article, Peer reviewed
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Deep convolutional neural networks have proven effective in computer vision, especially in the task of image classification. Nevertheless, the success is limited to supervised learning approaches, requiring extensive amounts of labeled training data that impose time-consuming manual efforts. Unsupervised deep learning methods were introduced to overcome this challenge. The gap, however, towards achieving comparable classification accuracy to supervised learning is still significant. This paper presents a deep learning framework for images of planktonic organisms with no ground truth or manually labeled data. This work combines feature extraction methods using state-of-the-art unsupervised training schemes with clustering algorithms to minimize the labeling effort while improving the classification process based on essential features learned by the deep learning model. The models utilized in the framework are tested over existing planktonic data sets. Empirical results show that unsupervised approaches that cluster the data based on the deep learning model’s feature space representations improve the classification task and can identify classes that have not been seen during the learning process.