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 changes in kernel shape and value. The proposed deformable dynamic convolution is compatible with the static convolution in terms of input–output relationships. The capability of existing network architectures can be enhanced by replacing the static convolution with the suggested deformable dynamic convolution. Extensive experiments demonstrate that the proposed deformable dynamic convolution can improve the network performance in various computer vision tasks, including image classification and semantic segmentation.