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  • title: Knowledge distillation via softmax regression representation learning
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            Knowledge distillation via softmax regression representation learning
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            Knowledge distillation via softmax regression representation learning

            May 3, 2021

            Speakers

            JY

            Jing Yang

            Speaker · 0 followers

            BM

            Brais Martinez

            Speaker · 0 followers

            AB

            Adrian Bulat

            Speaker · 0 followers

            About

            This paper addresses the problem of model compression via knowledge distillation. We advocate for a method that optimizes the output feature of the penultimate layer of the student network and hence is directly related to representation learning. Previous distillation methods which typically impose direct feature matching between the student and the teacher do not take into account the classification problem at hand. On the contrary, our distillation method decouples representation learning and…

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            I2

            ICLR 2021

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            About ICLR 2021

            The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.

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