While conceptually simple and easy to train, Imagen yields surprisingly strong results. Imagen outperforms other methods on COCO [38] with zero-shot FID-30K of 7.27, significantly outperforming prior work such asGLIDE [43](at 12.4) and the concurrent work ofDALL-E 2[56](at 10.4).

Frequently,transformations occurring in data can be better represented by a subset of a group than by agroup asawhole, e.g., rotations in[ 90,90 ]. Insuch cases, amodel that respects equivariancepartially is better suited to represent the data.

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In this work, we aim to leverage prior symbolic knowledge to improve the performance ofdeepmodels.

The conditions on the representation that imply global convergence are different between these two algorithms.

Explicit density evaluation is required in many statistical procedures, while synthesis of novel examples can enable agents to imagine and plan in an environment prior tochoosing aaction.

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Reinforcement Learning (RL) has achievedimpressiveprogress inrecent years, such asresults in Atari [24] and Go [28] in which RL agents even perform better than human beings.