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ACM Faculty Talk Series

Semi-supervised Learning for Visual Recognition

ACM Faculty Talk Series - 2

Semi-supervised Learning for Visual Recognition

Dr. Hamed Pirsiavash, Assistant Professor, CSEE 
1 pm - 2 pm Friday, February 23, 2018, ITE 325, UMBC
   

We are interested in learning representations (features) that are discriminative for semantic image understanding tasks such as object classification, detection, and segmentation in images. A common approach to obtain such features is to use supervised learning. However, this requires manual annotation of images, which is costly, time-consuming, and prone to errors. In contrast, unsupervised or self-supervised feature learning methods exploiting unlabeled data can be much more scalable and flexible. I will present some of our efforts in this direction.



Regards,
ACM Committee

**Please email 'npillai1@umbc.edu '  with any questions regarding this event.
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Posted: February 21, 2018, 11:44 AM