The bandit problem with graph feedback, proposed in [Mannor and Shamir, NeurIPS 2011], is modeled by a directed graphG = (V,E) where V is the collection of bandit arms, and once an arm is triggered, all its incident arms are observed.

Recent advances in text-to-image generation have enabled the creation of high-quality images with diverse applications.

Foreffectiveknowledge transfer,weadopt the idea of domain classifier so that student training is guided by discriminative features invariant totherepresentational space shift between teacher andstudent.

Existing deeplearning-based demoiréing methods trainedonlargescaledatasets are limited in handling various complex moiré patterns, and mainly focus on demoiréing of photos taken of digital displays.

Generated video plans are then grounded to visual-motor control, through an inverse dynamics model that infers actions from generated videos.

By onlytraining agenerativemodel inanunsupervised way,theframeworkutilizes the data distribution to build a compressor. Using a compressor-based distance metric derived from Kolmogorov complexity, together with few labeled data, NPC-LVclassifies without further training.