『论文阅读』Image-to-Image Translation with Conditional Adversarial Networks

来源:Image-to-Image Translation with Conditional Adversarial Nets

源码:Github

这篇文章主要提供了一个基于cGAN的模型,并且利用这个general的模型可以同时应用到多个任务场景中去,而不需要额外设置模型结果和目标函数。

  • Photos to semantic segmentation
  • Cityscapes labels to photos
  • Colorization
  • Facades labels to photo
  • Day to night
  • The edges to photo
  • And so on.
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『Leetcode』Combination Sum IV

Given an integer array with all positive numbers and no duplicates, find the number of possible combinations that add up to a positive integer target.

Example

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nums = [1, 2, 3]
target = 4

The possible combination ways are:
(1, 1, 1, 1)
(1, 1, 2)
(1, 2, 1)
(1, 3)
(2, 1, 1)
(2, 2)
(3, 1)

Note that different sequences are counted as different combinations.

Therefore the output is 7.
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『Leetcode』Spiral Matrix

Given a matrix of m x n elements (m rows, n columns), return all elements of the matrix in spiral order.

For example, Given the following matrix:

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[
[1, 2, 3]
[4, 5, 6]
[7, 8, 9]
]

You should return [1,2,3,6,9,8,7,4,5].

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『论文阅读』Generating Videos With Scene Dynamics

来源:NIPS2016

这篇论文提出了一个基于GAN的网络模型,同时可以进行视频识别和视频生成的task。

Task

  Video recognition  & video Generation.

  即利用一些Unlabeled videos 去训练模型同时解决识别问题和视频生成的任务。

实验表明:

  1. 模型可以生成一些短小的视频并且效果较好

  2. 可以根据static image 预测之后的图片序列

  3. 模型学习到的特征可以很好的用来进行图片分类

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