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  • title: MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning
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            MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning
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            MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning

            May 3, 2021

            Speakers

            NF

            Nanyi Fei

            Speaker · 0 followers

            ZL

            Zhiwu Lu

            Speaker · 0 followers

            TX

            Tao Xiang

            Speaker · 0 followers

            About

            Most recent few-shot learning (FSL) approaches are based on episodic training whereby each episode samples few training instances (shots) per class to imitate the test condition. However, this strict adhering to test condition has a negative side effect, that is, the trained model is susceptible to the poor sampling of few shots. In this work, for the first time, this problem is addressed by exploiting inter-episode relationships. Specifically, a novel meta-learning via modeling episode-level re…

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            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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