Salt Pepper噪声以及使用median filter来减少噪声

处理图像时,我们用的图片往往都会有很多噪声。在黑暗中或是设备感光器受到影响,拍出来的图像就会有很多噪声,俗称“噪点”,Salt & Pepper就是其中一种。为什么会叫盐和胡椒粉?因为这些噪声不是白的就是黑的,看起来很像是在图片上撒了盐和胡椒粉,而更专业点的会叫它脉冲噪声,这是在图像信号中突然且尖锐的(sudden and sharp)扰动导致图片变得粗糙。

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神经网络-初识Perceptron感知器

人工神经网络的实现和背后的思想,都是来自于人类大脑,初中生物课上讲膝跳反射其实就能很好地解释人脑是如何工作的。

上图可知,人类神经系统由三部分构成,Receptors接收到外部环境的刺激后,将这种刺激转换成电脉冲信号传送到Neural net,经过处理,将电脉冲信号发送给Effectors,最后生成可识别的响应。整个环节中有两个方向:

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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.

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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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