暂无分享,去创建一个
Mingkui Tan | Ian Reid | Chunhua Shen | Lingqiao Liu | Bohan Zhuang | I. Reid | Lingqiao Liu | Chunhua Shen | Mingkui Tan | Bohan Zhuang
[1] Philip Heng Wai Leong,et al. FINN: A Framework for Fast, Scalable Binarized Neural Network Inference , 2016, FPGA.
[2] Gang Yu,et al. BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation , 2018, ECCV.
[3] Swagath Venkataramani,et al. PACT: Parameterized Clipping Activation for Quantized Neural Networks , 2018, ArXiv.
[4] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Kaiming He,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Shuchang Zhou,et al. DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients , 2016, ArXiv.
[7] Li Fei-Fei,et al. Progressive Neural Architecture Search , 2017, ECCV.
[8] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[9] Zhijian Liu,et al. HAQ: Hardware-Aware Automated Quantization With Mixed Precision , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[10] 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.
[11] Tao Mei,et al. daBNN: A Super Fast Inference Framework for Binary Neural Networks on ARM devices , 2019, ACM Multimedia.
[12] Jian Sun,et al. ExFuse: Enhancing Feature Fusion for Semantic Segmentation , 2018, ECCV.
[13] Tony X. Han,et al. Learning Efficient Object Detection Models with Knowledge Distillation , 2017, NIPS.
[14] Quoc V. Le,et al. Neural Architecture Search with Reinforcement Learning , 2016, ICLR.
[15] George Papandreou,et al. Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.
[16] François Chollet,et al. Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Shwetak N. Patel,et al. Heterogeneous Bitwidth Binarization in Convolutional Neural Networks , 2018, NeurIPS.
[18] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[19] Avi Mendelson,et al. UNIQ , 2018, ACM Trans. Comput. Syst..
[20] Christoph Meinel,et al. BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet , 2017, ACM Multimedia.
[21] Ian D. Reid,et al. RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Linda G. Shapiro,et al. ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation , 2018, ECCV.
[23] Forrest N. Iandola,et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.
[24] Ali Farhadi,et al. YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Zhuowen Tu,et al. Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Ian D. Reid,et al. Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[28] Viktor K. Prasanna,et al. Analysis of high-performance floating-point arithmetic on FPGAs , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..
[29] Ke Wang,et al. AI Benchmark: Running Deep Neural Networks on Android Smartphones , 2018, ECCV Workshops.
[30] Wei Liu,et al. Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm , 2018, ECCV.
[31] Subhransu Maji,et al. Semantic contours from inverse detectors , 2011, 2011 International Conference on Computer Vision.
[32] Andreas Ehliar. Area efficient floating-point adder and multiplier with IEEE-754 compatible semantics , 2014, 2014 International Conference on Field-Programmable Technology (FPT).
[33] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[34] Georgios Tzimiropoulos,et al. Training Binary Neural Networks with Real-to-Binary Convolutions , 2020, ICLR.
[35] Wei Pan,et al. Towards Accurate Binary Convolutional Neural Network , 2017, NIPS.
[36] Yoshua Bengio,et al. BinaryConnect: Training Deep Neural Networks with binary weights during propagations , 2015, NIPS.
[37] Jae-Joon Han,et al. Learning to Quantize Deep Networks by Optimizing Quantization Intervals With Task Loss , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[39] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Junjie Yan,et al. Quantization Mimic: Towards Very Tiny CNN for Object Detection , 2018, ECCV.
[41] Yiming Yang,et al. DARTS: Differentiable Architecture Search , 2018, ICLR.
[42] Guosheng Lin,et al. Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] George Papandreou,et al. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.
[44] Quoc V. Le,et al. Efficient Neural Architecture Search via Parameter Sharing , 2018, ICML.
[45] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[46] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[48] Lin Xu,et al. Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights , 2017, ICLR.
[49] Ali Farhadi,et al. YOLOv3: An Incremental Improvement , 2018, ArXiv.
[50] Hao Chen,et al. Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[51] Gang Hua,et al. How to Train a Compact Binary Neural Network with High Accuracy? , 2017, AAAI.
[52] Yan Wang,et al. Fully Quantized Network for Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[53] Xiangyu Zhang,et al. Channel Pruning for Accelerating Very Deep Neural Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[54] Yu Bai,et al. ProxQuant: Quantized Neural Networks via Proximal Operators , 2018, ICLR.
[55] Zhenzhi Wu,et al. GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework , 2017, Neural Networks.
[56] Li Fei-Fei,et al. Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[57] Christoph Meinel,et al. Learning to Train a Binary Neural Network , 2018, ArXiv.
[58] Dan Alistarh,et al. Model compression via distillation and quantization , 2018, ICLR.
[59] Bingbing Ni,et al. Performance Guaranteed Network Acceleration via High-Order Residual Quantization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[60] James T. Kwok,et al. Loss-aware Binarization of Deep Networks , 2016, ICLR.
[61] Iasonas Kokkinos,et al. Fast, Exact and Multi-scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs , 2016, ECCV.
[62] James T. Kwok,et al. Loss-aware Weight Quantization of Deep Networks , 2018, ICLR.
[63] Oriol Vinyals,et al. Hierarchical Representations for Efficient Architecture Search , 2017, ICLR.
[64] Linda G. Shapiro,et al. ESPNetv2: A Light-Weight, Power Efficient, and General Purpose Convolutional Neural Network , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[65] Ran El-Yaniv,et al. Binarized Neural Networks , 2016, ArXiv.
[66] Bo Chen,et al. MnasNet: Platform-Aware Neural Architecture Search for Mobile , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[67] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[68] Christoph Meinel,et al. Training Competitive Binary Neural Networks from Scratch , 2018, ArXiv.
[69] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[70] Haichen Shen,et al. TVM: An Automated End-to-End Optimizing Compiler for Deep Learning , 2018, OSDI.
[71] Liujuan Cao,et al. Towards Optimal Structured CNN Pruning via Generative Adversarial Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[72] Ross B. Girshick,et al. Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[73] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[74] Ian D. Reid,et al. Towards Effective Low-Bitwidth Convolutional Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[75] Hao Chen,et al. FCOS: Fully Convolutional One-Stage Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[76] Quoc V. Le,et al. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.
[77] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[78] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[79] Xin Dong,et al. Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit? , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[80] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[81] Trevor Darrell,et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[82] Asit K. Mishra,et al. Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy , 2017, ICLR.
[83] Yurong Chen,et al. Network Sketching: Exploiting Binary Structure in Deep CNNs , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[84] Jing Liu,et al. Discrimination-aware Channel Pruning for Deep Neural Networks , 2018, NeurIPS.
[85] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[86] Bo Chen,et al. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[87] Wei Liu,et al. Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance , 2018, International Journal of Computer Vision.
[88] Philip Heng Wai Leong,et al. SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[89] Bin Liu,et al. Ternary Weight Networks , 2016, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[90] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[91] Wei Liu,et al. High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[92] Yoshua Bengio,et al. Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation , 2013, ArXiv.
[93] Eunhyeok Park,et al. Weighted-Entropy-Based Quantization for Deep Neural Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[94] Diana Marculescu,et al. Regularizing Activation Distribution for Training Binarized Deep Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[95] G. Hua,et al. LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks , 2018, ECCV.
[96] Song Han,et al. Trained Ternary Quantization , 2016, ICLR.
[97] Yuandong Tian,et al. Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search , 2018, ArXiv.
[98] Jian Sun,et al. Deep Learning with Low Precision by Half-Wave Gaussian Quantization , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[99] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[100] Ling Shao,et al. TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights , 2018, ECCV.