These paired GANs we called cycleGAN. If we use A domain as a picture style and B domain as another picture style. We can do Image-to-Image translation.
CycleCoopnet Our model we called CycleCoopnet. As we know the power of Coop-net, we try to use Coopnet to generate Image-to-Image Translation models. We use two CoopNets, and let the output of one Coopnet can be the input of another Coopnet. This idea is from CycleGAN, we know we can generate pictures from one style to another style by one generator. We use another generator to transform the style back and try to make the result of the transform cycle can be consistent with the origin picture.
1.3 Cooperative Learning Networks
The special of our model is using two different networks to do the cooperate training. In the working of Cooperative Learning networks [1], this paper introduces the main concept of Cooperative Learning networks by two different networks.
Cooperative Training also uses two networks to achieve our requirements. There are two networks in Cooperative Neural networks, we will call it ”CoopNet” in the following content. One is also called a generator, and another is called a descriptor. The main features of the generator generate a fake picture, and the generator is better when this generated picture is as similar as possible to the real picture. The main features of the descriptor are trying to describe the generated picture and real picture by an easy vector.
By comparing the similarity of these two vectors. We can evaluate the performance of our generator.
We also use an easy metaphor to introduce the role of these two networks. In our work, the generator plays the role of the student, the main task student should do is learning hard as good as possible. The generator’s task in this work is generating the picture as similar to a real picture. Another network is a descriptor, plays the role of the teacher.
The main task of the descriptor is trying to teach a student how to generate a real image.
By this concept, the teacher will learn the knowledge first, then try to teach a student about the knowledge. Knowledge is like a real picture. Descriptor learned the feature
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from the real picture first, then taught the generator the features of the picture. Students can learn the knowledge from the teacher’s teaching. This is similar to the generator learning the features from the descriptor teaching.
In the original cooperative training networks. The main goal is doing the vector-to-Image task. In our work, we also use cooperative networks, but we want to use these networks to solve the Image-to-Image Translation problem.
2 Related Work
Our work merged the concept of Cooperative Learning Networks [1] and cycleGAN’s Image-to-Image Translation works [8], we will separately introduce some related works in two parts of the following contents.
2.1 GAN and pix2pix
In our works, we choose the cooperative learning network(CoopNet) instead of the Gen-erative adversarial network(GAN). We consider in GAN’s generator and discriminator combination networks. The method of how we update generator and discriminator has let discriminator tell the picture generated by generator real or fake. We can see we only have a simple result of the discriminator’s result. We can easily use a confusion matrix to explain the updated concept of GAN. Table 1 shows the concept of how we update discriminator networks. Although we have a generator loss function to calculate the L1 distance to update generator variables, discriminator loss will be too simple that only decided from the right or wrong judgment.
We try to solve this problem to avoid the networks support the generator, that is, discriminator of GAN to be too simple. We use the descriptor of CoopNet to support our generator function. This is the main reason why we use Cooperative Learning Networks instead of GAN. In discriminator of CoopNet, we use the descriptor to change the gener-ated picture and real picture to another domain, that is, a vector expression of the picture.
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Table 1: Confusion matrix of GAN networks
Our descriptor convoluted the picture and wish the result vectors can be as similar as possible. We calculate all the differences and calculate the sum by all of the values.
This method can solve the problem we met in GAN’s discriminator. Since we use logit values to update discriminator, we consider the update will be restricted in a small scope. Our work use descriptor to support the generator leaning. Descriptor loss is not like logit values, it calculates the difference between all of the pixels. We consider this will be better than GAN’s support networks, discriminator.
The main task of this paper is using the cooperation method instead of the adversarial method to finish Image-to-Image Translation working. The difference from the CycleGAN working by using adversarial networks, we used cooperative networks in this work. On the other hand, we used cooperative networks to do Image-to-Image Translation, different from [1] using the cooperative networks to do vector-to-image Translation.
Merging two neural networks is the special of this model and also the evaluation of this work.
The method used in [8], we will call this model ”pix2pix” in the following content.
We use a different method to do the same work, even our work is better in efficiency and performance. In pix2pix architecture, it uses the generative adversarial network, we usually called it ”GAN”, to make the work realize. GAN also used two neural networks for the work, one is a generator, another is a discriminator. We can say this concept by a metaphor: Generator plays the role of the counterfeiter, and discriminator plays the role of detective. The mission counterfeiter is trying to make the counterfeit real picture to cheat the detective, make the detective cannot tell the difference from the real picture
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and counterfeit picture. On the other hand, the detective needs to tell which picture is real and which picture is fake. As you can see, the counterfeiter and detective are the competitors of each other. These two networks compete with each other and strengthen themselves when the generator can not cheat the discriminator or when the discriminator cannot tell the picture of real or fake. This is the main idea of the generative adversarial network(GAN).