Abstract
Convolutional neural networks (CNN) are popularly used for applications in natural language processing, video analysis and image recognition. However, the max-pooling layer used in CNNs discards most of the data, which is a drawback in applications, such as, prediction of video frames. With this in mind, we propose an adaptive prediction and classification network (APCN) based on a data-dependent pooling architecture. We formulate a combined cost function for minimizing prediction and classification errors. During testing, we identify a new class in an unsupervised fashion. Simulation results over a synthetic data set show that the APCN algorithm is able to learn the spatio-temporal information to predict and classify the video frames, as well as, identify a new class during testing.
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Acknowledgments
S. S. Garani acknowledges IISc-start up funds for this project.
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Machireddy, A., Garani, S.S. (2018). Data Dependent Adaptive Prediction and Classification of Video Sequences. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_14
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DOI: https://doi.org/10.1007/978-3-319-91253-0_14
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