Large scale GAN training for high fidelity natural image synthesis解讀
《Large scale GANtraining for high fidelity natural image synthesis》這篇文章對訓練大規模生成對抗網絡進行了實驗和理論分析，經過使用以前提出的一些技巧，如數據截斷、正交正則化等，保證了大型生成對抗網絡訓練過程的穩定性。本文訓練出的模型在生成數據的質量方面達到了史無前例的高度，遠超以前的方法。做者對生成對抗網絡訓練時的穩定性進行了分析，
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相關文章
- 1. LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS
- 2. bigGAN: LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS
- 3. 【PaddlePaddle論文復現】LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS閱讀心得
- 4. 飛槳論文復現之《LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS》
- 5. 飛槳PaddlePaddle論文復現：LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS
- 6. Paper之BigGAN：ICLR 2019最新論文《LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS》論文研究中
- 7. 【飛槳】學習論文復現，【Paddle Paddle】復現LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS
- 8. Large Scale Image Classification
- 9. High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
- 10. VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION