ASU Electronic Theses and Dissertations
- 1 English
- 1 Public
Machine learning models convert raw data in the form of video, images, audio, text, etc. into feature representations that are convenient for computational process- ing. Deep neural networks have proven to be very efficient feature extractors for a variety of machine learning tasks. Generative models based on deep neural networks introduce constraints on the feature space to learn transferable and disentangled rep- resentations. Transferable feature representations help in training machine learning models that are robust across different distributions of data. For example, with the application of transferable features in domain adaptation, models trained on a source distribution can be applied …
- Eusebio, Jose Miguel Ang, Panchanathan, Sethuraman, Davulcu, Hasan, et al.
- Created Date