Notes for Object Detection: One Stage Methods

In this post, we focus on two mainstreams of one-stage object detection methods: YOLO family and SSD family. Compared to two-stage methods (like R-CNN series), those models skip the region proposal stage and directly extract detection results from feature maps. For that reason, one-stage models are faster but at the cost of reduced accuracy.

From ProGAN to StyleGAN

In this post, we are looking into two high-resolution image generation models: ProGAN and StyleGAN. They generates the artificial image gradually, starting from a very low resolution and continuing to a high resolution (finally $1024\times 1024​$).

Notes for Adversarial Discriminative Domain Adaptation

About this paper

  • Title: Adversarial Discriminative Domain Adaptation
  • Authors: Eric Tzeng, Judy Hoffman, Kate Saenko, Trevor Darrell
  • Topic: Domain Adaptation
  • From: arXiv:1702.05464, appearing in CVPR 2017

Contributions

  • 将之前的论文里提到的一些方法,例如weight sharing、base models、adversarial loss等,归入了统一的框架之中,并进行了测试;
  • 提出了一种新的框架ADDA,主要思想是不做分类器的自适应,而是设法将目标域的数据映射到域源域差不多的特征空间上,这样就能够复用源域的分类器。

Something about GAN

最近在看关于GANs的论文,并且自己动手用PyTorch写了一些经典文章的实现,想要稍微总结一下,故有此文。在最后我总结了我自己看过的有关GANs的一些比较好的资源,希望对读者有所帮助。