Improved Techniques for Training Adaptive Deep Networks
暂无分享,去创建一个
Ruigang Yang | Hao Li | Xiaojuan Qi | Gao Huang | Hong Zhang | Gao Huang | Ruigang Yang | Xiaojuan Qi | Hong Zhang | Hao Li
[1] Noam Shazeer,et al. HydraNets: Specialized Dynamic Architectures for Efficient Inference , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[2] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[3] Guocong Song,et al. Collaborative Learning for Deep Neural Networks , 2018, NeurIPS.
[4] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Kilian Q. Weinberger,et al. CondenseNet: An Efficient DenseNet Using Learned Group Convolutions , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[6] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[7] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[8] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[9] Antoni B. Chan,et al. Incorporating Side Information by Adaptive Convolution , 2017, International Journal of Computer Vision.
[10] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Hanan Samet,et al. Pruning Filters for Efficient ConvNets , 2016, ICLR.
[12] Venkatesh Saligrama,et al. Adaptive Neural Networks for Fast Test-Time Prediction , 2017, ArXiv.
[13] Jiwen Lu,et al. Runtime Neural Pruning , 2017, NIPS.
[14] Jonathon Shlens,et al. Recurrent Segmentation for Variable Computational Budgets , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[15] Kaiming He,et al. Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[16] Shu Kong,et al. Pixel-wise Attentional Gating for Parsimonious Pixel Labeling , 2018, ArXiv.
[17] Rich Caruana,et al. Do Deep Nets Really Need to be Deep? , 2013, NIPS.
[18] H. T. Kung,et al. BranchyNet: Fast inference via early exiting from deep neural networks , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[19] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.
[20] Xin Wang,et al. SkipNet: Learning Dynamic Routing in Convolutional Networks , 2017, ECCV.
[21] Ran El-Yaniv,et al. Binarized Neural Networks , 2016, ArXiv.
[22] Rich Caruana,et al. Model compression , 2006, KDD '06.
[23] Larry S. Davis,et al. BlockDrop: Dynamic Inference Paths in Residual Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[24] Katerina Fragkiadaki,et al. Depth-Adaptive Computational Policies for Efficient Visual Tracking , 2017, EMMCVPR.
[25] Xu Lan,et al. Knowledge Distillation by On-the-Fly Native Ensemble , 2018, NeurIPS.
[26] Zhiqiang Shen,et al. Learning Efficient Convolutional Networks through Network Slimming , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[27] Zhichao Li,et al. Dynamic Computational Time for Visual Attention , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).
[28] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Yixin Chen,et al. Compressing Convolutional Neural Networks in the Frequency Domain , 2015, KDD.
[30] Serge J. Belongie,et al. Convolutional Networks with Adaptive Inference Graphs , 2017, International Journal of Computer Vision.
[31] Xiangyu Zhang,et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[32] Kilian Q. Weinberger,et al. Multi-Scale Dense Networks for Resource Efficient Image Classification , 2017, ICLR.
[33] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[34] Alex Graves,et al. Adaptive Computation Time for Recurrent Neural Networks , 2016, ArXiv.
[35] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[36] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[37] Xiangyu Zhang,et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design , 2018, ECCV.
[38] Li Zhang,et al. Spatially Adaptive Computation Time for Residual Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Song Han,et al. AMC: AutoML for Model Compression and Acceleration on Mobile Devices , 2018, ECCV.