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  <p>This is the official implementation of Adaptive Parametric Activation (APA) for ECCV2024 accepted paper. </p>
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  <p> <img src="./assets/unified_activations_combined.jpg"
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  alt="APA unifies most activation functions under the same formula."
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- style="float: left; margin-right: 10px; width="50%" height="auto""
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  /></p>
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  <h3>Abstract</h3>
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  <p>APA can be used insed the intermediate layers using Adaptive Generalised Linear Unit (AGLU): $AGLU(z,位,魏) = z APA(z,位,魏)$. The derivatives of AGLU with respect to 魏 (top), 位 (middle) and z (bottom) are shown below:
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  <img src="./assets/derivative_visualisations.jpg"
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  alt="APA unifies most activation functions under the same formula."
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- style="float: left; margin-right: 10px;"
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  /></p>
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  <h3> Simple Code implementation </h3>
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  <p>This is the official implementation of Adaptive Parametric Activation (APA) for ECCV2024 accepted paper. </p>
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  <p> <img src="./assets/unified_activations_combined.jpg"
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  alt="APA unifies most activation functions under the same formula."
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+ style="float: left; margin-right: 10px; width:50%; height:auto"
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  /></p>
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  <h3>Abstract</h3>
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  <p>APA can be used insed the intermediate layers using Adaptive Generalised Linear Unit (AGLU): $AGLU(z,位,魏) = z APA(z,位,魏)$. The derivatives of AGLU with respect to 魏 (top), 位 (middle) and z (bottom) are shown below:
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  <img src="./assets/derivative_visualisations.jpg"
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  alt="APA unifies most activation functions under the same formula."
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+ style="float: left; margin-right: 10px; width:50%; height:auto"
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  /></p>
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  <h3> Simple Code implementation </h3>
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