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Mining GOLD Samples for Conditional GANs Sangwoo Mo KAIST swmo Chiheon Kim Kakao Brain Sungwoong Kim Kakao Brain swkim Minsu Cho POSTECH mscho Jinwoo Shin KAIST, AItrics jinwoos Abstract Conditional generative adversarial networks cGANs have gained a considerable
Mining GOLD Samples for Conditional GANs as its advanced variant, the conditional GANs cGANs 31 have gained a considerable attention due to its classwise controllability 9, 42, 10 and superior quality for complex generation tasks 39, 33, 5. Training GANs including cGANs, however, are known to be often hard and highly
Conditional generative adversarial networks cGANs have gained a considerable attention in recent years due to its classwise controllability and superior quality for complex generation tasks. We introduce a simple yet effective approach to improving cGANs by measuring the discrepancy between the data distribution and the model distribution on given samples. The proposed measure, coined the
Mining GOLD Samples for Conditional GANs. PyTorch implementation of 34Mining GOLD Samples for Conditional GANs34 NeurIPS 2019.. Run experiments. Run example reweighting experiments. python name reweightbase dataset mnist epochs 20 mode acgansemi python name reweightgold dataset mnist epochs 20 mode acgansemigold
Mining GOLD Samples for Conditional GANs. Part of Advances in Neural Information Processing Systems 32 NIPS 2019 We propose three applications of the GOLD example reweighting, rejection sampling, and active learning, which improve the training, inference, and data selection of cGANs, respectively.
Mining GOLD Samples for Conditional GANs. NeurIPS 2019. Load More. NeurIPS Mining GOLD Samples for Conditional GANs. NeurIPS 2019. Optimal Sampling and Clustering in the Stochastic Block Model. NeurIPS 2019. Minimum Weight Perfect Matching via Blossom Belief Propagation. NeurIPS 2015
Improved Training of Generative Adversarial Networks Using Representative Features. 01282018 by Duhyeon Bang, et al. 0 share . Despite of the success of Generative Adversarial Networks GANs for image generation tasks, the tradeoff between image diversity and visual quality are an wellknown issue.
Request PDF Freeze Discriminator A Simple Baseline for Finetuning GANs Generative adversarial networks GANs have shown outstanding performance on a broad range of computer vision problems
Follow their code on GitHub. a Simple Baseline for FineTuning GANs CVPRW 2020 Python 78 7 gold. Mining GOLD Samples for Conditional GANs NeurIPS 2019 Python 7 3 331 contributions in the last year Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Sun Mon Tue Wed Thu Fri Sat. Learn how we count contributions.
This 34Cited by34 count includes citations to the following articles in Scholar. The ones marked may be different from the article in the profile. Add coauthors Coauthors. Mining GOLD Samples for Conditional GANs. S Mo, C Kim, S Kim, M Cho, J Shin. Advances in Neural Information Processing Systems, 61676178, 2019. 1
The uncaptioned dataset that we considered is the LSUN 32 dataset, which consists in around one million labeled images for each of the 10 scene categories and 20 object categories 32. From the
make a composite sample.22 After crushing, a 1.5 kg to 2 kg sample was split from the original sample and sent for testing. The result was meant to be representative of 20 t of broken ground. In addition, grab samples were also taken from cutandfill stopes during mining. Rogers22 notes that much of the gold at Dome was concentrated in quartz
The lifecycle of a gold mine. People in hard hats working underground is what often comes to mind when thinking about how gold is mined. Yet mining the ore is just one stage in a long and complex gold mining process. Long before any gold can be extracted, significant exploration and development needs to take place, both to determine, as accurately as possible, the size of the deposit as well
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