Current deep networks are very data-hungry and benefit from training on large-scale datasets, which are often time-consuming to collect and annotate.

Finally, to avoid trivial solutions and stabilize model training, we introduce a simple origin-guided loss. To validate the effectiveness ofour proposed method, we conducted experiments on tasks including image deraining, denoising, deblurring, and low-light image enhancement.

In recommender systems, the collected data used for training is always subject to selection bias, which poses a great challenge for unbiased learning.

Toovercome this bottleneck, data are collected via biased simulations that explore the state space more rapidly. Wepropose aframeworkforlearning frombiased simulations rooted in the infinitesimal generator of the process and the associated resolvent operator. Wecontrast our approach to more common ones based on the transfer operator, showing thatitcanprovably learn thespectral properties oftheunbiased system frombiaseddata.

Despitethehighdemand,high-quality 3D content remains relatively scarce.