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  • title: An Empirical Study of Adder Neural Networks for Object Detection
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            An Empirical Study of Adder Neural Networks for Object Detection
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            An Empirical Study of Adder Neural Networks for Object Detection

            6. prosince 2021

            Řečníci

            XC

            Xinghao Chen

            Sprecher:in · 0 Follower:innen

            CX

            Chang Xu

            Sprecher:in · 0 Follower:innen

            MD

            Minjing Dong

            Sprecher:in · 0 Follower:innen

            O prezentaci

            Adder neural networks (AdderNets) have shown impressive performance on image classification with only addition operations, which are more energy efficient than traditional convolutional neural networks built with multiplications. Compared with classification, there is a strong demand on reducing the energy consumption of modern object detectors via AdderNets for real-world applications such as autonomous driving and face detection. In this paper, we present an empirical study of AdderNets for ob…

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            NeurIPS 2021

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            O organizátorovi (NeurIPS 2021)

            Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

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