Extra Form
강연자 현윤석
소속 인하대학교
date 2022-11-17


In recent years, artificial intelligence has made remarkable progress in developing algorithms that can learn from vast amounts of carefully labeled data. This paradigm of supervised learning has made great success in training specialist models that perform extremely well on the task they were trained to do, but there’s a limit to building more intelligent, generalized models without a massive amount of labeled data.
Self-supervised learning is an unsupervised learning method where the supervised learning task is created out of the unlabeled input data. It enables machines to understand new concepts quickly after seeing only a few examples that are labeled.
In this seminar, we will briefly review the basic concepts of deep learning and computer vision, and explain the recent development of self-supervised learning methods, focusing on representation learning in the field of computer vision.


첨부 '1'
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  2. <정년퇴임 기념강연> Hardy, Beurling, and invariant subspaces

  3. <정년퇴임 기념강연> The Elements of Euclid

  4. <정년퇴임 기념강연> 리만 가설에 관련된 옌센 다항식의 영점

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  19. 19Nov
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    in 수학강연회

    <학부생을 위한 ɛ 강연> Self-Supervised Learning in Computer Vision

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