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MS Final Exam – Lawrence Neal

Open Set Learning with Counterfactual Images

In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. Counterfactual image generation is a dataset augmentation process based on generative adversarial networks which synthesizes examples that are close to training examples, yet do not belong to any known training category. By augmenting an image classifier with examples generated by this process, the open set recognition problem can be reformulated as classification with an additional class of novel and unknown examples, to improve performance on image classification in the presence of unknown classes.

Co Advisor: Fuxin Li
Co Advisor: Xiaoli Fern
Committee: Weng-Keen Wong
Committee: Alan Fern
GCR: Jimmy Yang

Tuesday, November 20, 2018 at 10:00am to 12:00pm

Kelley Engineering Center, 1005
110 SW Park Terrace, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic


College of Engineering, Electrical Engineering and Computer Science
Contact Name

Calvin Hughes

Contact Email

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