Existing permanental processes often impose constraints on kernel types or sta-tionarity, limiting the model's expressiveness. To overcome these limitations, we propose a novel approach utilizing the sparse spectral representation of non-stationary kernels.

Camera-based physiological measurement is a fast growing field of computer vision.

The SI2E analyzes value differences in the agent's policy between state-action

DSN primarily seeks to transfer knowledge to the new coming task from the learned tasks by selecting the affiliated weights of a small set of neurons to be activated, including the reused neurons from prior tasks via neuron-wise masks. And it also transfers possibly valuable knowledge to the earlier tasks via data-free replay.

It represents a radiance field using tri-planes with three multi-resolutions for each plane: 128, 256, and 512 in both height and width, and 32 in feature depth. However, any MDE model can be utilized within our framework [19, 13, 12]. The training process takes approximately 3 hours. In other words, we can rewrite the above scheme as a closed problem. The results of DDP-NeRF with in-domain priors are 20.96,

Project page is available at https://ku-cvlab.github.io/DaRF/ .