Publication
A Reinforcement Learning Approach for Sequential Spatial Transformer Networks
Fatemeh Azimi; Federico Raue; Jörn Hees; Andreas Dengel
In: ICANN. International Conference on Artificial Neural Networks (ICANN), 28th, September 17-19, Munich, Germany, Springer, 2019.
Abstract
Spatial Transformer Networks (STN) can generate geometric transformations which modify input images to improve classifier’s performance. In this work, we combine the idea of STN with Reinforcement Learning (RL). To this end, we break the affine transformation down into
a sequence of simple and discrete transformations. We formulate the task as a Markovian Decision Process (MDP) and use RL to solve this sequential decision-making problem. STN architectures learn the transformation parameters by minimizing the classification error and backpropagating the gradients through a sub-differentiable sampling module. In our method, we are not bound to differentiability of the sampling modules. Moreover, we have freedom in designing the objective rather than only minimizing the error; e.g., we can directly set the target is maximizing the accuracy. We design multiple experiments to verify the effectiveness of our method using cluttered MNIST and Fashion-MNIST datasets and show that our method outperforms STN with a proper definition of MDP components.