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Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich feelings and latent dispositions embedded in these media. In this work, we explore how Convolutional Neural Networks (CNNs), a now de facto computational machine learning tool particularly in the area of Computer Vision, can be specifically applied to the task of visual sentiment prediction. We accomplish this through fine-tuning experiments using a state-of-the-art CNN and via rigorous architecture analysis, we present several modifications that lead to accuracy improvements over prior art on a dataset of images from a popular social media platform. We additionally present visualizations of local patterns that the network learned to associate with image sentiment for insight into how visual positivity (or negativity) is perceived by the model. ; This work has been developed in the framework of the BigGraph TEC2013-43935-R project, funded by the Spanish Ministerio de Economía y Competitividad and the European Regional Development Fund (ERDF). It has been supported by the Severo Ochoa Program's SEV2015-0493 grant awarded by the Spanish Government, the TIN2015-65316 project by the Spanish Ministerio de Economía y Competitividad and contracts 2014-SGR-1051 by Generalitat de Catalunya. The Image Processing Group at the UPC is a SGR14 Consolidated Research Group recognized and sponsored by the Catalan Government (Generalitat de Catalunya) through its AGAUR office. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GeForce GTX Titan Z and X used in this work and the support of BSC/UPC NVIDIA GPU Center of Excellence. ; Peer Reviewed ; Postprint (author's final draft)

Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich feelings and latent dispositions embedded in these media. In this work, we explore how Convolutional Neural Networks (CNNs), a now de facto computational machine learning tool particularly in the area of Computer Vision, can be specifically applied to the task of visual sentiment prediction. We accomplish this through fine-tuning experiments using a state-of-the-art CNN and via rigorous architecture analysis, we present several modifications that lead to accuracy improvements over prior art on a dataset of images from a popular social media platform. We additionally present visualizations of local patterns that the network learned to associate with image sentiment for insight into how visual positivity (or negativity) is perceived by the model. ; This work has been developed in the framework of the BigGraph TEC2013-43935-R project, funded by the Spanish Ministerio de Economía y Competitividad and the European Regional Development Fund (ERDF). It has been supported by the Severo Ochoa Program's SEV2015-0493 grant awarded by the Spanish Government, the TIN2015-65316 project by the Spanish Ministerio de Economía y Competitividad and contracts 2014-SGR-1051 by Generalitat de Catalunya. The Image Processing Group at the UPC is a SGR14 Consolidated Research Group recognized and sponsored by the Catalan Government (Generalitat de Catalunya) through its AGAUR office. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GeForce GTX Titan Z and X used in this work and the support of BSC/UPC NVIDIA GPU Center of Excellence. ; Peer Reviewed ; Postprint (author's final draft)

Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long term dependencies. In backpropagation through time settings, these issues are tightly coupled with the large, sequential computational graph resulting from unfolding the RNN in time. We introduce the Skip RNN model which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph. This model can also be encouraged to perform fewer state updates through a budget constraint. We evaluate the proposed model on various tasks and show how it can reduce the number of required RNN updates while preserving, and sometimes even improving, the performance of the baseline RNN models. Source code is publicly available at https://imatge-upc.github.io/skiprnn-2017-telecombcn/ ; This work was partially supported by the Spanish Ministry of Economy and Competitivity and the European Regional Development Fund (ERDF) under contracts TEC2016-75976-R and TIN2015- 65316-P, by the BSC-CNS Severo Ochoa program SEV-2015-0493, and grant 2014-SGR-1051 by the Catalan Government. Víctor Campos was supported by Obra Social "la Caixa" through La Caixa-Severo Ochoa International Doctoral Fellowship program. We would also like to thank the technical support team at the Barcelona Supercomputing Center. ; Postprint (published version)

Deep learning algorithms base their success on building high learning capacity models with millions of parameters that are tuned in a data-driven fashion. These models are trained by processing millions of examples, so that the development of more accurate algorithms is usually limited by the throughput of the computing devices on which they are trained. In this work, we explore how the training of a state-of-the-art neural network for computer vision can be parallelized on a distributed GPU cluster. The effect of distributing the training process is addressed from two different points of view. First, the scalability of the task and its performance in the distributed setting are analyzed. Second, the impact of distributed training methods on the final accuracy of the models is studied. ; This work is partially supported by the Spanish Ministry of Economy and Competitivity under contract TIN2012-34557, by the BSC-CNS Severo Ochoa program (SEV-2011-00067), by the SGR programmes (2014-SGR-1051 and 2014-SGR-1421) of the Catalan Government and by the framework of the project BigGraph TEC2013-43935-R, funded by the Spanish Ministerio de Economia y Competitividad and the European Regional Development Fund (ERDF). We also would like to thank the technical support team at the Barcelona Supercomputing center (BSC) especially to Carlos Tripiana. ; Peer Reviewed ; Postprint (published version)

Deep learning algorithms base their success on building high learning capacity models with millions of parameters that are tuned in a data-driven fashion. These models are trained by processing millions of examples, so that the development of more accurate algorithms is usually limited by the throughput of the computing devices on which they are trained. In this work, we explore how the training of a state-of-the-art neural network for computer vision can be parallelized on a distributed GPU cluster. The effect of distributing the training process is addressed from two different points of view. First, the scalability of the task and its performance in the distributed setting are analyzed. Second, the impact of distributed training methods on the final accuracy of the models is studied. ; This work is partially supported by the Spanish Ministry of Economy and Competitivity under contract TIN2012-34557, by the BSC-CNS Severo Ochoa program (SEV-2011-00067), by the SGR programmes (2014-SGR-1051 and 2014-SGR-1421) of the Catalan Government and by the framework of the project BigGraph TEC2013-43935-R, funded by the Spanish Ministerio de Economia y Competitividad and the European Regional Development Fund (ERDF). We also would like to thank the technical support team at the Barcelona Supercomputing center (BSC) especially to Carlos Tripiana. ; Peer Reviewed ; Postprint (published version)