Squeeze-and-Excitation Networks
Tóm tắt
Từ khóa
Tài liệu tham khảo
shen, 2016, Places401 and places365 models
huang, 2016, Deep networks with stochastic depth, Proc Eur Conf Comput Vis, 646
krizhevsky, 2009, Learning multiple layers of features from tiny images, Tech Rep, 1
girshick, 2018, Detectron
baker, 2018, Accelerating neural architecture search using performance prediction, Proc Workshop Int Conf Learn Represent
lin, 2014, Microsoft COCO: Common objects in context, Proc Eur Conf Comput Vis, 740
brock, 2018, SMASH: One-shot model architecture search through hypernetworks, Proc Int Conf Learn Representations
cubuk, 2018, Autoaugment: Learning augmentation policies from data, Proc IEEE Conf Comput Vis Pattern Recognit
liu, 2019, DARTS: Differentiable architecture search, Proc Int Conf Learn Represent
elsken, 2019, Efficient multi-objective neural architecture search via lamarckian evolution, Proc Int Conf Learn Represent
real, 2017, Large-scale evolution of image classifiers, Proc Int Conf Mach Learn, 2902
saxena, 2016, Convolutional neural fabrics, Proc Conf Neural Inf Process Syst, 4053
negrinho, 0, Deeparchitect: Automatically designing and training deep architectures, arXiv 1704 08792
liu, 2018, Progressive neural architecture search, Proc Eur Conf Comput Vis, 19
bergstra, 2012, Random search for hyper-parameter optimization, J Mach Learn Res, 13, 281
yang, 2009, Linear spatial pyramid matching using sparse coding for image classification, Proc IEEE Conf Comput Vis Pattern Recognit, 3431
nair, 2010, Rectified linear units improve restricted boltzmann machines, Proc 27th Int Conf Mach Learn, 807
jozefowicz, 2015, An empirical exploration of recurrent network architectures, Proc 32nd Int Conf Mach Learn, 37, 2342
howard, 2017, MobileNets: Efficient convolutional neural networks for mobile vision applications, arXiv 1704 04861
gastaldi, 2017, Shake-shake regularization, arXiv 1705 07485
devries, 2017, Improved regularization of convolutional neural networks with cutout, arXiv 1708 04552
krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, Proc Conf Neural Inf Process Syst, 84
szegedy, 2016, Inception-v4, inception-resnet and the impact of residual connections on learning, Proc AAAI Conf Artif Intell, 4278
lin, 2014, Network in network, Proc Int Conf Learn Representations
miller, 1989, Designing neural networks using genetic algorithms, Proc 3rd Int Conf Genetic Algorithms, 379
mnih, 2014, Recurrent models of visual attention, Proc Conf Neural Inf Process Syst, 2204
vaswani, 2017, Attention is all you need, Proc Conf Neural Inf Process Syst, 5998
woo, 2018, CBAM: Convolutional block attention module, Proc Eur Conf Comput Vis, 3
xu, 2015, Show, attend and tell: Neural image caption generation with visual attention, Proc Int Conf Mach Learn, 2048
miech, 2017, Learnable pooling with context gating for video classification, Proc IEEE Conf Comput Vis Pattern Recognit
bluche, 2016, Joint line segmentation and transcription for end-to-end handwritten paragraph recognition, Proc Conf Neural Inf Process Syst, 838
simonyan, 2015, Very deep convolutional networks for large-scale image recognition, Proc Int Conf Learn Representations
baker, 2017, Designing neural network architectures using reinforcement learning, Proc Int Conf Learn Representations
santurkar, 2018, How does batch normalization help optimization? (no, it is not about internal covariate shift), Proc Conf Neural Inf Process Syst, 2483
he, 2016, Identity mappings in deep residual networks, Proc Eur Conf Comput Vis, 630
srivastava, 2015, Training very deep networks, Proc Conf Neural Inf Process Syst, 2377
yosinski, 2014, How transferable are features in deep neural networks?, Proc Conf Neural Inf Process Syst, 3320
chen, 2017, Dual path networks, Proc Conf Neural Inf Process Syst, 4467
hu, 2018, Gather-excite: Exploiting feature context in convolutional neural networks, Proc Conf Neural Inf Process Syst, 9401
morcos, 2018, On the importance of single directions for generalization, Proc Int Conf Learn Representations
mahajan, 2018, Exploring the limits of weakly supervised pretraining, Proc Eur Conf Comput Vis, 185
ren, 2015, Faster R-CNN: Towards real-time object detection with region proposal networks, Proc 28th Int Conf Neural Inf Process Syst, 1, 91
ioffe, 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift, Proc 32nd Int Conf Mach Learn, 37, 448
newell, 2016, Stacked hourglass networks for human pose estimation, Proc Eur Conf Comput Vis, 483
larochelle, 2010, Learning to combine foveal glimpses with a third-order Boltzmann machine, Proc Conf Neural Inf Process Syst, 1243
jaderberg, 2015, Spatial transformer networks, Proc Conf Neural Inf Process Syst, 2017
tan, 2018, Mnasnet: Platform-aware neural architecture search for mobile, arXiv 1807 11626
zoph, 2017, Neural architecture search with reinforcement learning, Proc Int Conf Learn Representations
pham, 2018, Efficient neural architecture search via parameter sharing, Proc Int Conf Mach Learn, 4095
liu, 2018, Hierarchical representations for efficient architecture search, Proc Int Conf Learn Representations