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With the rapid development of neural architecture search (NAS), researchers found powerful network architectures for a wide range of vision tasks.
1907
Earlier work this paper cites.
1907
Earlier work this paper cites.
1910
Earlier work this paper cites.
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: A simple way to prevent neural networks from overfitting. JMLR
1958
Earlier work this paper cites.
Stanley KO, Miikkulainen R (2002) Evolving neural networks through augmenting topologies. Evolutionary Computation
2002
Earlier work this paper cites.
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: A large-scale hierarchical image database. In: CVPR
2009
Earlier work this paper cites.
Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images. Tech. rep., Citeseer
2009
Earlier work this paper cites.
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: NIPS
2012
Earlier work this paper cites.
Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: ECCV
2014
Earlier work this paper cites.
Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: ICML
2015
Earlier work this paper cites.
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature
2015
Earlier work this paper cites.
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, et al. (2015) ImageNet large scale visual recognition challenge. IJCV
2015
Earlier work this paper cites.
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: ICLR
2015
Earlier work this paper cites.
Srivastava RK, Greff K, Schmidhuber J (2015) Training very deep networks. In: NIPS
2015
Earlier work this paper cites.
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: CVPR
2015
Earlier work this paper cites.
Zheng L, Shen L, Tian L, Wang S, Wang J, Tian Q (2015) Scalable person re-identification: A benchmark. In: ICCV
2015
Earlier work this paper cites.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR
2016
Earlier work this paper cites.
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In: ECCV
2016
Earlier work this paper cites.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: CVPR
2016
Cited alongside, same era.
Zagoruyko S, Komodakis N (2016) Wide residual networks. arXiv:1605.07146
2016
Cited alongside, same era.
Baker B, Gupta O, Naik N, Raskar R (2017) Designing neural network architectures using reinforcement learning. In: ICLR
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Pham H, Guan MY, Zoph B, Le QV, Dean J (2018) Efficient neural architecture search via parameter sharing. In: ICML
2018
Later among the works it cites.
2018
Later among the works it cites.
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: CVPR
2018
Later among the works it cites.
Sun Y, Zheng L, Yang Y, Tian Q, Wang S (2018) Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In: ECCV
2018
Later among the works it cites.
Wang H, Zhu X, Gong S, Xiang T (2018) Person re-identification in identity regression space. IJCV
2018
Later among the works it cites.
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2017
Cited alongside, same era.
Han D, Kim J, Kim J (2017) Deep pyramidal residual networks. In: CVPR
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: CVPR
2017
Cited alongside, same era.
Larsson G, Maire M, Shakhnarovich G (2017) FractalNet: Ultra-deep neural networks without residuals. In: ICLR
2017
Cited alongside, same era.
Suganuma M, Shirakawa S, Nagao T (2017) A genetic programming approach to designing convolutional neural network architectures. In: GECCO
2017
Cited alongside, same era.
Xie L, Yuille A (2017) Genetic CNN. In: ICCV
2017
Cited alongside, same era.
Zheng Z, Zheng L, Yang Y (2017) Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In: ICCV
2017
Cited alongside, same era.
Wei L, Zhang S, Gao W, Tian Q (2018) Person transfer gan to bridge domain gap for person re-identification. In: CVPR
2018
Later among the works it cites.
Zhang X, Zhou X, Lin M, Sun J (2018) ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In: CVPR
2018
Later among the works it cites.
Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. In: CVPR
2018
Later among the works it cites.
Cai H, Zhu L, Han S (2019) ProxylessNAS: Direct neural architecture search on target task and hardware. In: ICLR
2019
Closest in time.
Chen X, Xie L, Wu J, Tian Q (2019) Progressive differentiable architecture search: Bridging the depth gap between search and evaluation. In: ICCV
2019
Closest in time.
Dong X, Yang Y (2019) Searching for a robust neural architecture in four gpu hours. In: CVPR
2019
Closest in time.
Duan K, Bai S, Xie L, Qi H, Huang Q, Tian Q (2019) Centernet: Keypoint triplets for object detection. In: ICCV
2019
Closest in time.
Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, et al. (2019) Pytorch: An imperative style, high-performance deep learning library. In: NeurIPS
2019
Closest in time.
Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: ICML
2019
Closest in time.
Tan M, Chen B, Pang R, Vasudevan V, Sandler M, Howard A, Le QV (2019) Mnasnet: Platform-aware neural architecture search for mobile. In: CVPR
2019
Closest in time.
Wu B, Dai X, Zhang P, Wang Y, Sun F, Wu Y, Tian Y, Vajda P, Jia Y, Keutzer K (2019) Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search. In: CVPR
2019
Closest in time.
Xie S, Zheng H, Liu C, Lin L (2019) SNAS: Stochastic neural architecture search. In: ICLR
2019
Closest in time.