Abstract:.The shape and values of a typical static convolution kernel remain fixed once the network is trained. Recently, dynamic convolutions were proposed to change the kernel’s values depending on the input during the test phase. We aim to extend the concept of dynamic convolutions by introducing an element-wise dynamic convolution approach. This method enables adaptive changes in kernel values for each output data element. Furthermore, a deformable element-wise dynamic convolution is proposed to enable simultaneous… Show more
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