Handling Label Sparsity: A Semi-Unsupervised Approach

In the realm of computer science, the acquisition of large-scale datasets has been greatly facilitated by advancements in sensor technologies and the ubiquity of the Internet. This influx of data has led to the widespread adoption of Deep Learning (DL) techniques for various data processing tasks. However, achieving high-quality predictions using DL approaches is challenging, primarily due to two factors: the substantial annotation requirements and the need for comprehensive coverage of data aspects, which are often impractical or impossible. In practical scenarios, there is a demand for an approach that can classify data with minimal annotated samples for a defined number of categories while also identifying unknown and unlabeled categories. For instance, while a beekeeper can collect vast amounts of (temporal) high-resolution data from an apiary, creating a comprehensive dataset for honey bees is challenging due to their complexity. Similarly, in laboratory settings, inferring cell types from tissue samples can be challenging due to the presence of unknown or rare cell types, despite the availability of large and accurate reference datasets. Recently, Semi-Unsupervised Learning (SuSL) has emerged as a promising paradigm that leverages both labeled and unlabeled data to enhance (DL) model performance. However, the current Multi-Layer Perceptron (MLP) implementation of SuSL has limitations when applied to real-world processes or organisms, such as honey bees. This thesis addresses these limitations by proposing novel methods to enhance the practical applicability of SuSL: (1A) Establishing a generalized encoder-decoder framework for SuSL, enabling seamless integration with various neural network architectures without manual feature extraction. (1B) Investigating critical parameters and dataset characteristics to understand their impact on prediction tasks in a standardized setup. (2) Addressing dataset imbalance in both labeled and unlabeled subsets. (3) Modeling diverse tabular input data while maintaining a compact representation within the network for downstream tasks. (4) Efficiently processing raw sensor data, including time-dependent sensor relations, without manual feature extraction. In addition to theoretical developments and benchmarking results, this thesis demonstrates the real-world applicability of the proposed SuSL implementation through two case studies in biology: (1) Analysis of a billion-tick dataset from ground-level observations of honey bees: from backend to explorative data analysis to SuSL usage. (2) Application of SuSL on newly recorded and unlabeled data using a large reference atlas for cell type annotation: from data integration to SuSL application. Through these contributions, this research aims to advance the practical utility of SuSL in real-world scenarios, particularly in domains with complex and heterogeneous data.