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The parameter $k$ decides the coverage of interaction, and in ECA the kernel size $k$ is adaptively determined from the channel dimensionality $C$ instead of by manual tuning, using cross-validation:\r\n\\begin{equation}\r\n    k = \\psi(C) = \\left | \\frac{\\log_2(C)}{\\gamma}+\\frac{b}{\\gamma}\\right |_\\text{odd}\r\n\\end{equation}\r\n\r\nwhere $\\gamma$ and $b$ are hyperparameters. $|x|_\\text{odd}$ indicates the nearest odd function of $x$. \r\n\r\nCompared to SENet, ECANet has an \r\nimproved excitation module, and provides an efficient and effective block which can readily be \r\n incorporated into various\r\nCNNs.","description_state":"present","introduced_year":null,"introduced_by":{"title":"ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks","paper":"/paper/eca-net-efficient-channel-attention-for-deep","first_author":"Qilong 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