论文阅读：Co-Evolutionary Compression for Unpaired Image Translation
论文阅读：Co-Evolutionary Compression for Unpaired Image Translation Abstract 作者提出了一种新的协同进化方法来同时减少内存使用和FLOP。在实践中，两个图像域的生成器被编码为两个群体且协同优化来迭代地研究最重要的卷积滤波器。用参数的数量、鉴别器感知正则化和循环一致性来计算每个个体的适应度。 Method 作者指出，传统网络压缩使用
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相关文章
- 1. 论文阅读《Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks》
- 2. 论文阅读——CariGANs: Unpaired Photo-to-Caricature Translation
- 3. CrossNet: Latent Cross-Consistency for Unpaired Image Translation
- 4. 阅读论文《Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs》
- 5. 【论文阅读】Diverse Image-to-Image Translation via Disentangled Representations（ECCV2018 oral）...
- 6. 论文笔记《Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks》
- 7. 【论文笔记】cycleGAN：《Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks》
- 8. 论文Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
- 9. 论文阅读——Dual Learning for Machine Translation
- 10. 论文阅读：Dual Learning for Machine Translation