Abstract: Sampling theory inspires a lot of approaches in signal processing society. In particular, reconstructions of an analog signal from discretized samples were quite important for us to understand a system. As deep learning era has begun, we are surrounded by tremendous digital datasets and now is the time to reconstruct such accumulated digital datasets up to their preferences. In this talk, I will reshape a typical DAC(digital-to-analog) into a neural networks to handle continuous representations. This will be done by implicit neural representation models. Single image super resolution (SISR) and image warping are demonstrated with the proposed regime.