MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images

This paper is aimed at creating extremely small and fast convolutional neural networks (CNN) for the problem of facial expression recognition (FER) from frontal face images. To this end, we employed the popular knowledge distillation (KD) method and identified two major shortcomings with its use: 1) a fine-grained grid search is needed for tuning the temperature hyperparameter and 2) to find the optimal size-accuracy balance, one needs to search for the final network size (or the compression rate). On the other hand, KD is proved to be useful for model compression for the FER problem, and we discovered that its effects gets more and more significant with the decreasing model size. In addition, we hypothesized that translation invariance achieved using max-pooling layers would not be useful for the FER problem as the expressions are sensitive to small, pixel-wise changes around the eye and the mouth. However, we have found an intriguing improvement on generalization when max-pooling is used. We conducted experiments on two widely-used FER datasets, CK+ and Oulu-CASIA. Our smallest model (MicroExpNet), obtained using knowledge distillation, is less than 1MB in size and works at 1851 frames per second on an Intel i7 CPU. Despite being less accurate than the state-of-the-art, MicroExpNet still provides significant insights for designing a microarchitecture for the FER problem.Comment: International Conference on Image Processing Theory, Tools and Applications (IPTA) 2019 camera ready version. Codes are available at: https://github.com/cuguilke/microexpne

Çok çekirdekli mimarilerde seyrek üçgen doğrusal sistemlerin paralel çözümü.

Many large-scale applications in science and engineering require the solution of sparse linear systems. One well-known approach is to solve these systems by factorizing the coefficient matrix into nonsingular sparse triangular matrices and solving the resulting sparse triangular systems via backward and forward sweep (substitution) operations. This can be considered as a direct solver or it is part of the preconditioning operation in an iterative scheme if incomplete factorization is computed. Often, these sparse triangular systems are the main performance bottleneck due to their inherently sequential nature. With the emergence of multi-core platforms, the interest in solving sparse triangular linear systems effectively in parallel has grown. In this thesis, a parallel sparse triangular linear system solver based on the generalization of Spike algorithm is proposed. The performance constraints of the proposed algorithm and their impacts on the performance are evaluated on matrices from different application domains. Furthermore, performance comparisons are made against the state-of-the-art parallel sparse triangular solver of Intel's Math Kernel Library.M.S. - Master of Scienc