In these frameworks, templates are constructed using an iterative process of template estimation and alignment, which is often computationally very expensive. Due in part to this shortcoming, most methods compute asingle template for the entire population of images, or a few templates for specific sub-groups of the data.

Ninedifferentdebiasing algorithms (and a baseline) have been evaluated with this dataset using the popular ResNet-18 network[36]. CelebA contains faces of celebrities with several binary task labelsandtwoprotected labels(genderandyouth). Table 3showsthe prediction results from a biased binary classifier and its bias values using the seven metrics. Without losing generality, we consider "Sport" the positive class in the binary classifier. Following the DP formula in Appendix A.2, for the "Sport" class, thePPRfemale is 45.0% (90 /200), andPPRmale is65.0%

Emerging ethical approaches have attempted to filter pretraining material, but such approaches have been ad hoc and failed to take context into account. We offer an approach to filtering grounded in law, which has directly addressed the tradeoffs in filtering material.

Severalworksextend thesetodynamic scenes captured with monocular video, with promising performance.

RNNPool can be applied to any tensor structuredproblem,butwefocuson2Dimages for ease of exposition.

We demonstrate the effectiveness of our algorithm by generating high-quality results of different target domains, including those with extremely few examples (e.g., 10).

Reward allocation, also known as the credit assignment problem, has been an important topic ineconomics, engineering, andmachine learning.

Apart from linguistic transformations, synthesizing among statements usually requires specific synthesis rulesofstatements.

As inthe classical problem, weights are fixed by an adversary and elements appear in random order. In contrast to previous variants of predictions, our algorithm only has access toamuch weakerpiece ofinformation: anadditive gapc.