Question: Is there a comparison to Xie et al. (2018) needed?
Evidence: We present the results of our new learning process applied to the CIFAR-10, Celeb-A, and ImageNet benchmark datasets, using the image sizes 32 × 32, 64 × 64, and 128 × 128 respectively. A sketch of the hybrid learning algorithm is given in Appendix D. Besides standard deep learning techniques for stable optimization, our framework is fully described by the ML objective and MCMC initialization above. We use the SNGAN architectures for all models, where EBMs use the SNGAN discriminator with no normalization. The generator has batch normalization for the CIFAR-10 and Celeb-A experiments only. Table displays the FID scores achieved by our model in comparison with prior methods.
When calculating FID scores, samples are first initialized from the generator then updated with the EBM using a number of Langevin updates tuned to provide optimal synthesis quality. As mentioned before, an appealing aspect of EBM learning with generator initialization is the ease of generating new images from scratch, in contrast with persistent initialization. We use 50K samples with the official FID code from to calculate all FID scores. In particular, our scores and framework are consistent with the GAN replication library from. We publicly release the checkpoints, learning code, and FID code for each model. The checkpoints are representative of what is achievable within an ordinary run of the code provided. Our CIFAR-10 results show a significant improvement over prior EBM synthesis and contribute to closing the gap between EBMs and other generative models. Surprisingly, our ImageNet results surpass the results of GANs such as SNGAN and SSGAN on unconditional synthesis using a similar magnitude of computational resources.
We present the results of our new learning process applied to the CIFAR-10, Celeb-A, and ImageNet benchmark datasets, using the image sizes 32 × 32, 64 × 64, and 128 × 128 respectively. A sketch of the hybrid learning algorithm is given in Appendix D. Besides standard deep learning techniques for stable optimization, our framework is fully described by the ML objective and MCMC initialization above. We use the SNGAN architectures for all models, where EBMs use the SNGAN discriminator with no normalization. The generator has batch normalization for the CIFAR-10 and Celeb-A experiments only. Table displays the FID scores achieved by our model in comparison with prior methods.
When calculating FID scores, samples are first initialized from the generator then updated with the EBM using a number of Langevin updates tuned to provide optimal synthesis quality. As mentioned before, an appealing aspect of EBM learning with generator initialization is the ease of generating new images from scratch, in contrast with persistent initialization. We use 50K samples with the official FID code from to calculate all FID scores. In particular, our scores and framework are consistent with the GAN replication library from. We publicly release the checkpoints, learning code, and FID code for each model. The checkpoints are representative of what is achievable within an ordinary run of the code provided. Our CIFAR-10 results show a significant improvement over prior EBM synthesis and contribute to closing the gap between EBMs and other generative models. Surprisingly, our ImageNet results surpass the results of GANs such as SNGAN and SSGAN on unconditional synthesis using a similar magnitude of computational resources.
Table 1: Comparison of FID scores among representative generative models. (*=EBM, †=conditional)****Xie et al. (2018)* 33.6
Answer: The original publication of Xie et al. (2018) did not report FID on CIFAR-10. We have since found that the scores can be found in a separate submission [a]. The FID score for CIFAR-10 is 33. Our method yields significant improvement. We will include this in future versions.