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Pixel Club: Learning like Humans Do, with Limited Training Data
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Amit Alfassy (EE, Technion)
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Tuesday, 23.2.2021, 11:30
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Zoom Lecture: https://technion.zoom.us/j/95741652165
While Deep learning has brought a huge advancement to computer vision, for most tasks we still need hundreds of labeled samples per class. The few-shot learning tasks attempts to alleviate the data problem by learning from 1/ 5 samples per class. We will discuss the few-shot learning domain through two of my papers. The first paper LaSO, is a SOTA augmentation mechanic for multi-label few-shot classification and was published in CVPR 2019. The second paper StarNet is the SOTA weakly-supervised few-shot object localization and detection method and was presented in AAAI 2021. Short bio Amit is a direct Ph.D. candidate at the Electrical engineering faculty under the supervision of prof. Alex Bronstein from the CS faculty. Amit also works part-time at IBM research AI.
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