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We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints.
A real-time algorithm for signal analysis with the help of the wavelet transform
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Spatial pyramid pooling in deep convolutional networks for visual recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2014
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Joint semantic segmentation and 3d reconstruction from monocular video
Kundu, A., Li, Y., Dellaert, F., Li, F., Rehg, J.M.: · 2014
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Flattened convolutional neural networks for feedforward acceleration
Jin, J., Dundar, A., Culurciello, E.: · 2014
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Jaderberg, M., Vedaldi, A., Zisserman, A.: · 2014
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Fixed-point feedforward deep neural network design using weights 1, 0, and -1
Hwang, K., Sung, W.: · 2014
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Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
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Deep-dive analysis of the data analytics workload in cloudsuite
Yasin, A., Ben-Asher, Y., Mendelson, A.: · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: · 2014
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Menze, M., Geiger, A.: · 2015
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., et al.: · 2015
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Compressing neural networks with the hashing trick
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Sparse convolutional neural networks
Liu, B., Wang, M., Foroosh, H., Tappen, M., Pensky, M.: · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2015
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Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: · 2015
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Convolutional feature masking for joint object and stuff segmentation
Dai, J., He, K., Sun, J.: · 2015
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
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Learning deconvolution network for semantic segmentation
Noh, H., Hong, S., Han, B.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Performance characterization of high-level programming models for gpu graph analytics
Wu, Y., Wang, Y., Pan, Y., Yang, C., Owens, J.D.: · 2015
Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: · 2017
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DA-RNN: Semantic mapping with data associated recurrent neural networks
Xiang, Y., Fox, D.: · 2017
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Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: · 2017
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Xception: Deep learning with depthwise separable convolutions
Chollet, F.: · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: · 2017
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Dilated residual networks
Yu, F., Koltun, V., Funkhouser, T.: · 2017
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Adam: A method for stochastic optimization
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The cityscapes dataset for semantic urban scene understanding
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: · 2016
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Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V.: · 2016
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Multi-scale context aggregation by dilated convolutions
Yu, F., Koltun, V.: · 2016
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Icnet for real-time semantic segmentation on high-resolution images
Zhao, H., Qi, X., Shen, X., Shi, J., Jia, J.: · 2017
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Lcnn: Lookup-based convolutional neural network
Bagherinezhad, H., Rastegari, M., Farhadi, A.: · 2017
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Badrinarayanan, V., Kendall, A., Cipolla, R.: · 2017
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Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
Lin, G., Milan, A., Shen, C., Reid, I.: · 2017
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The mapillary vistas dataset for semantic understanding of street scenes
Neuhold, G., Ollmann, T., Rota Bulò, S., Kontschieder, P.: · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: · 2017
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2018
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Zhang, X., Zhou, X., Lin, M., Sun, J.: · 2018
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Erfnet: Efficient residual factorized convnet for real-time semantic segmentation
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Learning to segment breast biopsy whole slide images
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Understanding convolution for semantic segmentation
Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., Cottrell, G.: · 2018
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Learning structured sparsity in deep neural networks
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