Author Archives: jqichina

Idea from paper reading

Posted on by
  1. “Specifically, we introduce strategies such as more augmentation, better learning rate schedules and label smoothing, that significantly improve the closed-set performance of the MSP baseline (sec. 4). We also propose the use of the maximum logit score (MLS), rather than normalized softmax probabilities, as an open-set indicator. With these adjustments, we push the baseline to become competitive with or outperform state-of-the-art OSR methods, substantially outperforming the currently reported baseline figures. Notably, we surpass state-of-the-art figures on four of the six OSR benchmark datasets.” in paper “OPEN-SET RECOGNITION: A GOOD CLOSED-SET CLASSIFIER IS ALL YOU NEED?”(ICLR2022): Obtained: general training strategy (such as augmentation, learning rate schedule, label smoothing, maximum logit score) is important to have better results.

2014 in review

Posted on by

WordPress.com Stats Helper Monkey 为此博客制作了一份 2014 年度报告。

下面是一段摘要：

一辆旧金山叮当车能搭载 60 位乘客。2014 年，此博客的浏览量约为 490 次。若用旧金山叮当车来运载这些乘客，差不多要运 8 趟。

点击此处查看完整报告。