論文閱讀：Co-Evolutionary Compression for Unpaired Image Translation
論文閱讀：Co-Evolutionary Compression for Unpaired Image Translation Abstract 作者提出了一種新的協同進化方法來同時減少內存使用和FLOP。在實踐中，兩個圖像域的生成器被編碼爲兩個羣體且協同優化來迭代地研究最重要的卷積濾波器。用參數的數量、鑑別器感知正則化和循環一致性來計算每個個體的適應度。 Method 作者指出，傳統網絡壓縮使用
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相關文章
- 1. 論文閱讀《Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks》
- 2. 論文閱讀——CariGANs: Unpaired Photo-to-Caricature Translation
- 3. CrossNet: Latent Cross-Consistency for Unpaired Image Translation
- 4. 閱讀論文《Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs》
- 5. 【論文閱讀】Diverse Image-to-Image Translation via Disentangled Representations（ECCV2018 oral）...
- 6. 論文筆記《Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks》
- 7. 【論文筆記】cycleGAN：《Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks》
- 8. 論文Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- 9. 論文閱讀——Dual Learning for Machine Translation
- 10. 論文閱讀：Dual Learning for Machine Translation