article · Imperial College Research Computing Service Data Repository · 2023
Learning from limited labeled data
Lazarou, Michalis
Cited by 0Open access
Abstract
A fundamental bottleneck of deep learning models that inhibits their use in many real world applications is their reliance on large labeled datasets. This thesis addresses this limitation by proposing several ideas that can improve the performance of deep learning models when the available labeled data is limited. The thesis proposes a novel algorithm for semi-supervised and transductive inference when the labeled data is limited achieving state of the art performance in the process. The proposed algorithm leverages the manifold structure of the labeled and unlabeled data to assign accurate labels to the unlabeled data before a label cleaning module is used to select the most confident pseudo-labeled data to be used as labeled data and augment the labeled dataset. Additionally, in real world situations the unlabeled data may contain noisy images. Using the unnormalized manifold similarity obtained from label propagation along with the label cleaning module, the aforementioned problem can be addressed effectively and filter the noisy data. Moreover, the thesis identifies a limitation of the traditional label propagation algorithm and proposes a novel variant that assigns more accurate labels to the unlabeled data. The idea is to iteratively optimize the positions of the labeled data in the manifold by minimizing a differentiable loss function. The thesis also investigates the performance of deep learning models when the available unlabeled data is class-imbalanced. A novel algorithm is proposed that achieves state of the art performance by combining effectively the two main lines of research in transductive learning for limited labeled data, which are: class centroid refinement and data manifold exploitation. Lastly, in certain applications obtaining data is extremely difficult. A novel generative model is proposed to generate synthetic data and train a classifier on both real and synthetic data.
Cite this paper
Lazarou, M. (2023). Learning from limited labeled data. Imperial College Research Computing Service Data Repository. https://doi.org/10.25560/109873
Lazarou, Michalis. “Learning from limited labeled data.” Imperial College Research Computing Service Data Repository, 2023, https://doi.org/10.25560/109873.
Lazarou, Michalis. 2023. “Learning from limited labeled data.” Imperial College Research Computing Service Data Repository. https://doi.org/10.25560/109873.
Lazarou, M. (2023) ‘Learning from limited labeled data’, Imperial College Research Computing Service Data Repository. Available at: https://doi.org/10.25560/109873.
@article{lazarou2023learning,
title = {{Learning from limited labeled data}},
author = {Lazarou, Michalis},
journal = {Imperial College Research Computing Service Data Repository},
year = {2023},
doi = {10.25560/109873}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.