Interpolating between Optimal Transport and MMD using Sinkhorn Divergences

Reading notes on Interpolating between Optimal Transport and MMD using Sinkhorn Divergences

The purpose of this paper is to show that the Sinkhorn divergences are convex, smooth, positive definite loss functions that metrize the convergence in law.

Countless methods in machine learning and image processing reley on comparisons betwen probability distributions. But simple dissimilarities such as the Total Variation norm or the Kullback-Leibler relative entropy do not take into account the distance d on the feature space χ\chi. As a result, they do not metrize the convergence in law and are unstable with respect to deformations of the distributions’ support. Optimal Transport distances (sometimes refeered as Earth Mover’s Distance) and Maximum Mean Discrepancies are continuous with respect to convergence in law and metrize its topology when feature space χ\chi is compact.

Read More

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.

Read More

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。

Read More

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.

Read More

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。

Read More