You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: We evaluate our models on ImageNet ILSVRC 2012 (Russakovsky et al., 2015) at 128\times128, 256\times256, and 512\times512 resolutions, employing the settings from Table 1, row 8.The samples generated by our models are presented in Figure 4, with additional samples in Appendix A, and online222https://drive.google.com/drive/folders/1lWC6XEPD0LT5KUnPXeve_kWeY-FxH002.We report IS and FID in Table 2. As our models are able to trade sample variety for quality, it is unclear how best to compare against prior art; we accordingly report values at three settings, with complete curves in Appendix D. First, we report the FID/IS values at the truncation setting which attains the best FID. Second, we report the FID at the truncation setting for which our model’s IS is the same as that attained by the real validation data, reasoning that this is a passable measure of maximum sample variety achieved while still achieving a good level of “objectness.” Third, we report FID at the maximum IS achieved by each model, to demonstrate how much variety must be traded off to maximize quality. In all three cases, our models outperform the previous state-of-the-art IS and FID scores achieved by Miyato et al. (2018) and Zhang et al. (2018).

Question: What is the truncation setting?
First, the FID/IS values at the truncation setting attain the best FID. Second, the FID at the truncation setting for which our model’s IS is the same as that acquired by the genuine validation data, reasoning that this is a satisfactory measure of maximal sample variety produced while still obtaining a fair degree of “objectness.” Third, FID at the greatest IS attained by each model, indicates how much variation must be traded off to optimize quality.