Fetching the paper…
Reading the bibliography…
Lightweight or mobile neural networks used for real-time computer vision tasks contain fewer parameters than normal networks, which lead to a constrained performance.
Srivastava, N., Hinton, G., Krizhevsky, A.: ’Dropout: A simple way to prevent neural networks from overfitting’, Journal of Machine Learning Research
1958
Earlier work this paper cites.
Lecun, Y., Bottou, L., Bengio, Y., et al. : ’Gradient-based learning applied to document recognition’, Proceedings of the IEEE
1998
Earlier work this paper cites.
Krizhevsky, A.: ’Learning multiple layers of features from tiny images’. Master’s thesis, University of Toronto, 2009
2009
Earlier work this paper cites.
Nair, V., Hinton, G.: ’ Rectified linear units improve restricted boltzmann machines’, International Conference on Machine Learning
2010
Earlier work this paper cites.
Glorot, X., Bordes, A., Bengio, Y.: ’Deep sparse rectifier neural networks’, Proceedings of the fourteenth international conference on artificial intelligence and statistics
2011
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.: ’Imagenet classification with deep convolutional neural networks’, Advances in Neural Information Processing Systems
2012
Earlier work this paper cites.
Hannun, A., Maas, A., Ng, A.:’Rectifier nonlinearities improve neural network acoustic models’, ICML Workshop on Deep Learning for Audio, Speech and Language Processing
2013
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., et al. : ’Delving deep into rectifiers: Surpassing human-level performance on imagenet classification’, IEEE International Conference on Computer Vision (ICCV)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Redmon, J., Divvala, S., Girshick, R., et al. : "You Only Look Once: Unified, Real-Time Object Detection", 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Earlier work this paper cites.
Held, D., Thrun, S., Savarese, S.: ’Learning to Track at 100 FPS with Deep Regression Networks’, European Conference on Computer Vision
2016
Cited alongside, same era.
Clevert, D., Unterthiner, T., Hochreiter, S.: ’Fast and accurate deep network learning by exponential linear units (elus)’, International Conference on Learning Representations
2016
Cited alongside, same era.
Jin, X., Xu, C., Feng, X., et al. : ’Deep learning with s-shaped rectified linear activation unit’, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence
2016
Cited alongside, same era.
2016
Cited alongside, same era.
He, K., Zhang, X., Ren, S., et al. : ’Deep Residual Learning for Image Recognition’, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Sandler, M., Howard, A., Zhu, M., et al. : "MobileNetV2: Inverted Residuals and Linear Bottlenecks", 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
Zhang, X., Zhou, X., Lin, M., et al. : ’Shufflenet: An extremely efficient convolutional neural network for mobile devices’, 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
2016
Cited alongside, same era.
’Self-normalizing neural networks’, https://arxiv.org/abs/1706.02515, accessed 8 June 2017
2017
Cited alongside, same era.
’Searching for activation functions’, https://arxiv.org/abs/1710.05941, accessed 16 October 2017
2017
Cited alongside, same era.
’Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms’, https://github.com/zalandoresearch/fashion-mnist, accessed 26 August 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Later among the works it cites.
Ma, N., Zhang, X., Zheng, H., et al. : ’Shufflenet V2: practical guidelines for efficient CNN architecture design’, European Conference on Computer Vision
2018
Later among the works it cites.
Li, H., Xiong, P., Fan, H., et al. : "DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation", 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2019
Later among the works it cites.
Bolya, D., Zhou, C., Xiao, F., et al. : ’YOLACT: Real-time Instance Segmentation’, IEEE International Conference on Computer Vision (ICCV)
2019
Later among the works it cites.
2019
Later among the works it cites.
’Cifar-zoo: Pytorch implementation of cnns for cifar dataset’, https://github.com/BIGBALLON/CIFAR-ZOO, accessed 14 November 2019
2019
Later among the works it cites.