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OCSVM-based Evaluation Method for Generative Neural Networks

Humanity has desired to create machines that can comprehend fine arts for a long while. Recently, several articles report neural network models that managed to create something which can be considered as art, such as paintings or music. Naturally, these proposals are also leveraged in the field of robotics and autonomous vehicles for tasks such as training environment generation and efficient exploration in reinforcement learning. The common feature of all these approaches is the utilization of generative neural networks. These generative models are a trendy and actively researched area of machine learning methods. However, it is hard to find a general and objective metric for the evaluation of such models. In this paper, a One-Class Support Vector Machine-based solution is proposed. The basic concept is demonstrated via experiments on a set of GANs. The presented method can be extended and refined in further, more exhaustive studies to serve as an alternative for the current state-of-the-art procedures.