Why are ConvNets beating traditional computer vision? 

　　图像识别（Image classification）的任务是将给定的图像正确的分类为预先定义的种类。传统方法，将该过程分为两个模块：feature extraction 以及 classification。

　    Feature Extraction：involves extracting a higher level of information from raw pixel values that can capture the distinction among the categories involved. This feature extraction is done in an unsupervised manner wherein the classes of the image have nothing to do with information extracted from pixels. Some of the traditional and widely used features are GIST, HOG, SIFT, LBP etc. After the feature is extracted, a classification module is trained with the images and their associated labels. A few examples of this module are SVM, Logistic Regression, Random Forest, decision trees etc. 

　　但是这个流程的问题在于：特征提取的过程无法根据 classes 和 images 进行微调（the feature extraction cannot be tweaked according to the classes and images）。所以，如果这个选中的特征缺乏表达性来区分种类，模型分类的准确度可定不会很好，不管你采用哪种分类的策略。A common theme among the state of the art following the traditional pipeline has been, to pick multiple feature extractors and club them inventively to get a better feature. But this involves too many heuristics as well as manual labor to tweak parameters according to the domain to reach a decent level of accuracy. By decent I mean, reaching close to human level accuracy. That’s why it took years to build a good computer vision system(like OCR, face verification, image classifiers, object detectors etc), that can work with a wide variety of data encountered during practical application, using traditional computer vision. We once produced better results using ConvNets for a company(a client of my start-up) in 6 weeks, which took them close to a year to achieve using traditional computer vision. 

　　Another problem with this method is that it is completely different from how we humans learn to recognize things. Just after birth, a child is incapable of perceiving his surroundings, but as he progresses and processes data, he learns to identify things. This is the philosophy behind deep learning, wherein no hard-coded feature extractor is built in. It combines the extraction and classification modules into one integrated system and it learns to extract, by discriminating representations from the images and classify them based on supervised data.

　　In order to understand the design philosophy of ConvNets, one must ask: What is the objective here ?  

　　a. Accuracy : 

　　If you are building an intelligent machine, it is absolutely critical that it must be as accurate as possible. One fair question to ask here is that ‘accuracy not only depends on the network but also on the amount of data available for training’. Hence, these networks are compared on a standard dataset called ImageNet.

　　ImageNet project is an ongoing effort and currently has 14,197,122 images from 21841 different categories. Since 2010, ImageNet has been running an annual competition in visual recognition where participants are provided with 1.2 million images belonging to 1000 different classes from Imagenet data-set. So, each network architecture reports accuracy using these 1.2 million images of 1000 classes.

　　b. Computation:

　　Most ConvNets have huge memory and computation requirements, especially while training. Hence, this becomes an important concern. Similarly, the size of the final trained model becomes an important to consider if you are looking to deploy a model to run locally on mobile. As you can guess, it takes a more computationally intensive network to produce more accuracy. So, there is always a trade-off between accuracy and computation.

　　Apart from these, there are many other factors like ease of training, the ability of a network to generalize well etc. The networks described below are the most popular ones and are presented in the order that they were published and also had increasingly better accuracy from the earlier ones.

AlexNet

This architecture was one of the first deep networks to push ImageNet Classification accuracy by a significant stride in comparison to traditional methodologies. It is composed of 5 convolutional layers followed by 3 fully connected layers, as depicted in Figure 1.

AlexNet, proposed by Alex Krizhevsky, uses ReLu(Rectified Linear Unit) for the non-linear part, instead of a Tanh or Sigmoid function which was the earlier standard for traditional neural networks. ReLu is given by 

f(x) = max(0,x)

The advantage of the ReLu over sigmoid is that it trains much faster than the latter because the derivative of sigmoid becomes very small in the saturating region and therefore the updates to the weights almost vanish(Figure 4). This is called vanishing gradient problem.

In the network, ReLu layer is put after each and every convolutional and fully-connected layers(FC). 

Another problem that this architecture solved was reducing the over-fitting by using a Dropout layer after every FC layer. Dropout layer has a probability,(p), associated with it and is applied at every neuron of the response map separately. It randomly switches off the activation with the probability p, as can be seen in figure 5.   

Why does DropOut work?

The idea behind the dropout is similar to the model ensembles. Due to the dropout layer, different sets of neurons which are switched off, represent a different architecture and all these different architectures are trained in parallel with weight given to each subset and the summation of weights being one. For n neurons attached to DropOut, the number of subset architectures formed is 2^n. So it amounts to prediction being averaged over these ensembles of models. This provides a structured model regularization which helps in avoiding the over-fitting. Another view of DropOut being helpful is that since neurons are randomly chosen, they tend to avoid developing co-adaptations among themselves thereby enabling them to develop meaningful features, independent of others.

VGG16

This architecture is from VGG group, Oxford. It makes the improvement over AlexNet by replacing large kernel-sized filters(11 and 5 in the first and second convolutional layer, respectively) with multiple 3X3 kernel-sized filters one after another. With a given receptive field(the effective area size of input image on which output depends), multiple stacked smaller size kernel is better than the one with a larger size kernel because multiple non-linear layers increases the depth of the network which enables it to learn more complex features, and that too at a lower cost. 

For example, three 3X3 filters on top of each other with stride 1 ha a receptive size of 7, but the number of parameters involved is 3*(9C^2) in comparison to 49C^2 parameters of kernels with a size of 7. Here, it is assumed that the number of input and output channel of layers is C.Also, 3X3 kernels help in retaining finer level properties of the image. The network architecture is given in the table.

You can see that in VGG-D, there are blocks with same filter size applied multiple times to extract more complex and representative features. This concept of blocks/modules became a common theme in the networks after VGG.

The VGG convolutional layers are followed by 3 fully connected layers. The width of the network starts at a small value of 64 and increases by a factor of 2 after every sub-sampling/pooling layer. It achieves the top-5 accuracy of 92.3 % on ImageNet.