DeepEMD A Few-Shot Image Classification method

A reading note on DeepEMD: Few-Shot Image Classification with Differentiable Earth Mover’s Distance and Structured Classifiers.

Illustration of Earth mover distance

Problems

Deep Neural Networks achieve high performance under the large labelled datasets. For some circumstances, no enough labelled images are provided. One of most well-studied machine learning algorithms is few-shot image classification. Only with small labelled data, few-shot algorithms can categorize new images.

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我的2021

2021已经结束了,又到了秀各种年终总结的时候,那么也不多我这一份。不知道大家在总结2021年时,是满怀成就感呢,还是懊悔自己某些事没做或者做错了,还是浑浑噩噩,没啥可圈可点呢?对于我来讲,这一年很充实。

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What are the difficulties in avoiding direct discrimination on the basis of a protected characteristic (e.g. gender) when creating AI systems?

This is my formative writing task in Ethics and Regulation of Artificial Intelligence(LAWM161) at University of Surrey.

Professor: Mikolaj Barczentewicz

Over the past few years, lots of services or products with artificial intelligence have come to people’s life. After several waves of research in Artificial Intelligence, some AI technologies have been emerging like Machine Learning, Deep learning and Neural Networks. Although great progress has been made, research on ethics and laws still lags behind the development of technology. Besides, it turns out that prejudice and discrimination between people will also appear in AI systems easily due to the wide spread of AI systems. There are many reasons for discrimination. In the training process of the AI model, some inappropriate training data may be used, which may contain discrimination information. At the same time, the designer of the AI system will also entrain some personal emotions when designing. Or the idea that the decision made by the Ai system is discriminatory. Even the wrong use of the AI system by users will make them feel treated differently. In this article, I will focus on the direct discrimination when creating an AI system, and discuss some difficulties to decrease direct discrimination.

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关于Laravel框架中Guard的底层实现

1. 什么是Guard

Laravel/Lumen框架中,用户的登录/注册的认证基本都已经封装好了,开箱即用。而登录/注册认证的核心就是:

  1. 用户的注册信息存入数据库(登记)
  2. 从数据库中读取数据和用户输入的对比(认证)

上述两步是登录/注册的基本,可以看到都会涉及到数据库的操作,这两步框架底层已经帮我们做好了,而且考虑到了很多情况,比如用户认证的数据表不是user表而是admin_user,认证字段是phone而不是email,等等一些问题都是Guard所要解决的,通过Guard可以指定使用哪个数据表什么字段等,Guard能非常灵活的构建一套自己的认证体系。

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记一次在Lloyds银行存钱的奇葩经历

一大早骑车去Lloyds银行存钱,本想着能很快搞定,结果钱被ATM“吃”了,也算是一次特殊的经历~

Lloyds银行ATM存钱一次最多只能50张,不管什么面值。今天存钱带了100张5英镑,也没有提前分成2份,存的时候我直接拿出一半塞了进去,结果机子出问题,提示机子出了问题无法识别我的钱,让我联系工作人员。我之前存超出的钱直接吐出来了,这次结果机子出问题。工作人员过来说我塞的钱超过50张了,让我等一会,她去叫同事开机子把钱拿出来。

这个同事等级应该比较高,需要她输入密码。之后,她过去把银行入口处的大门给关了,想出去的人无法出去,我想这是为了防止钱被抢了,不让人跑,整个过程大概持续了5分钟(一个人也别想跑,嘿嘿嘿)。

钱取出来后,工作人员让我去柜台办理,当时排队的人也少,很快就轮到我了。柜台办理和国内有些不同,这边像刷卡消费一样,插入POS机输入PIN码,之后就可以把卡收起来了,然后把要存的钱给他,拿到收据走人。办理流程也就3分钟。哈哈,以后直接去柜台了。最后给工作人员点个赞。