Views Can Be Deceiving: Improved SSL Through Feature Space Augmentation

Publication
The Twelfth International Conference on Learning Representations
Kimia Hamidieh
Kimia Hamidieh

Kimia is a PhD student at MIT EECS. Her research focuses on understanding how self-supervised pre-training strategies represent data to build models that generalize well out-of-distribution, as well as developing post-training strategies that ensure safety of models. She previously received her MSc at the University of Toronto and her BSc from Sharif University of Technology.

Haoran Zhang
Haoran Zhang

Haoran is a fourth year PhD student in EECS at MIT. He is generally interested in building robust machine learning models that maintain their performance and fairness across out-of-distribution environments, as well as applying such models to the healthcare setting. Haoran previously received his M.Sc. at the University of Toronto under the co-supervision of Dr. Marzyeh Ghassemi and Dr. Quaid Morris, and his B.Eng. from McMaster University.

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