Solutions to small datasets in defects detection based on Markov chain Monte Carlo simulations

In the processes of industrial defects detections, deep neural networks have received much favor for its high efficiency and low labor cost. As is known, a big amount of dataset is the precondition to train any network. However, the datasets of industrial defects tended to be small and hard to obtain. To solve the problem above, we raise a thought of simulation based on MCMC aiming to sample from the posterior distribution of the defect’s images, which is viewed as the limit distribution of certain Markov Chain. Once we capture the posterior distribution, the law of the defects is revealed. 1Therefore, generating pseudo samples by the law is quite reasonable. It has been proved that the algorithm is practicable by advanced devices and economical in the long term. The significance of this work is to raise a practicable thought towards the problem of mini-batch industrial datasets. Code is available at https://github.com/MrSmallWang/solutions-tosmall- datasets.