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License Generator Halcon 11
OpenEB is composed of 6 fundamental software modules released under open source license. They enable anyone to get a better understanding of Event-Based Vision, directly interact with events and build their own applications or plugins.
The latest release includes enhancements to help speed up time to production, allowing developers to stream their first events in minutes, or even build their own event camera from scratch using the provided camera plugins under open-source license as a base.
GAN adopts an unsupervised learning mode, the core aim of which is to make two neural networks confront each other to achieve the purpose of learning. This is mainly composed of generator G and discriminator D. The purpose of the generator is to generate an image that is as similar to the original image as possible from zero after the input noise z is continuously trained by the model, and to try to confuse the discriminator. Through mutual improvement in the process of the continuous game, the discriminator has strong discrimination ability, but there is no way to distinguish whether the input data are true or false. In this case, it can be determined that the generator has mastered the feature distribution of the original image through training.
The setting of the learning rate at different stages will seriously affect the training speed of the model. The higher the learning rate, the faster the learning speed. However, in order to prevent an over-fitting phenomenon, the learning rate is generally set lower. During the training process, not only are the generators of a single stage trained, but some generators nearby this stage are also trained at the same time. In addition, the learning rate at the lower stages of the training process is generally set relatively low.
Figure 2 shows the change of the generator and its learning rate during model training. Firstly, a maximum of three generators are set to be trained at the same time. If there are more than three generators, only the generators of the first three scales are trained, and the parameters of other generators remain unchanged. Secondly, the learning rate of the generator at the lower two stages is adjusted to 1/10 and 1/100. Figure 2 illustrates the four top-down stages of the training process. From the beginning of line 1 to the end of line 3, the number of generators is positively correlated with the number of training stages. In the fourth stage, the parameters of G0 are fixed, and the learning rates of G1, G2, and G3 are, respectively, set as 1%, 10%, and 100% of the original values.
With the above improvement measures, the iteration number of a certain stage has increased for the generator, but the iteration number of most stages has decreased. The total number of iterations of the model will be significantly reduced, and the training time will be greatly shortened, thus improving the training performance of the SinGAN model.
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