Deep Learning Using Tiny Domain-Specific Datasets with Sparse Labels - Smith, Thomas J (2021)
Machine learning is an ever-expanding field of research, and recently deep learning has been the architecture of choice. However, traditional deep learning methodologies require substantial amounts of data to train their networks. This requirement for large data means that there are large numbers of real-world problems that cannot utilise the power of these deep learning networks due to a lack of data. Being able to use deep learning architectures with tiny domain-specific datasets would allow sectors such as healthcare to use deep learning as an aid in training and potentially in real time procedures.