论文阅读：Dual Supervised Learning
论文阅读：Dual Supervised Learning 概述： 许多有监督的学习任务是对偶的，比如从英语翻译成汉语，从汉语翻译成中文；再如图像的分类和图像的生成。但是人们总是分别训练两个模型， 没有充分利用对偶的双方之间存在的关系，本文提出可以同时训练对偶的模型，且与分别训练两个模型相比可以提高性能。 介绍： 两个对偶问题可以看成空间到的映射以及到的映射。使用概率语言描述，原有的任务（prim
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相关文章
- 1. 论文阅读：Dual Supervised Learning for Natural Language Understanding and Generation
- 2. 论文阅读——Dual Learning for Machine Translation
- 3. 论文阅读：Dual Learning for Machine Translation
- 4. 论文阅读：Learning Dual Convolutional Neural Networks for Low-Level Vision
- 5. 论文阅读笔记（一）：Learning Dual Convolutional Neural Networks for Low-Level Vision
- 6. 论文阅读（【CVPR2018】Jinshan Pan - Learning Dual Convolutional Neural Networks for Low-Level Vision）
- 7. 论文阅读：IL2M: Class Incremental Learning With Dual Memory
- 8. 【论文阅读】Semi-supervised Transfer Learning for Image Rain Removal
- 9. Disentangled Variational Auto-Encoder for Semi-supervised Learning论文阅读
- 10. 论文阅读笔记 | (IJCAI 2018 Oral) Collaborative Learning for Weakly Supervised Object Detection