Built on

Krizhevsky, A.: Learning Multiple Layers of Features from Tiny Images. Tech. rep. (2009)
2009
Earlier work this paper cites.
Jaderberg, M., Vedaldi, A., Zisserman, A.: Speeding up convolutional neural networks with low rank expansions. arXiv preprint arXiv:1405.3866 (2014)
Original
2014
Earlier work this paper cites.
Courbariaux, M., Bengio, Y., David, J.P.: Binaryconnect: Training deep neural networks with binary weights during propagations. In: NIPS (2015)
2015
Earlier work this paper cites.
Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: NIPS (2015)
2015
Earlier work this paper cites.
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)
Original
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.: Imagenet large scale visual recognition challenge. IJCV 115
2015
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Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830 (2016)
Original
2016
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Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: Alexnet-level accuracy with 50x fewer parameters and < < 0.5mb model size. arXiv:1602.07360 (2016)
Original
2016
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Li, F., Zhang, B., Liu, B.: Ternary weight networks. arXiv preprint arXiv:1605.04711 (2016)
Original
2016
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Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)
Original
2016
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Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: Xnor-net: Imagenet classification using binary convolutional neural networks. In: ECCV (2016)
2016
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Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions (2016)
2016
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Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Original
2017
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Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. In: ICLR (2017), https://arxiv.org/abs/1611.01144
Original
2017
Earlier work this paper cites.
Lin, X., Zhao, C., Pan, W.: Towards accurate binary convolutional neural network. In: NIPS (2017)
2017
Earlier work this paper cites.
Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. In: ICLR (2017), https://openreview.net/forum?id=r1Ue8Hcxg
2017
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Bender, G., Kindermans, P.J., Zoph, B., Vasudevan, V., Le, Q.: Understanding and simplifying one-shot architecture search. In: ICML (2018)
2018
Earlier work this paper cites.

Similar

Chen, Y., Meng, G., Zhang, Q., Zhang, X., Song, L., Xiang, S., Pan, C.: Joint neural architecture search and quantization. ArXiv abs/1811.09426
Original
2018
Cited alongside, same era.
Dong, J.D., Cheng, A.C., Juan, D.C., Wei, W., Sun, M.: Dpp-net: Device-aware progressive search for pareto-optimal neural architectures. In: ECCV (2018)
2018
Cited alongside, same era.
Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L.J., Fei-Fei, L., Yuille, A., Huang, J., Murphy, K.: Progressive neural architecture search. In: ECCV (2018)
2018
Cited alongside, same era.
Liu, H., Simonyan, K., Vinyals, O., Fernando, C., Kavukcuoglu, K.: Hierarchical representations for efficient architecture search. In: ICLR (2018), https://openreview.net/forum?id=BJQRKzbA-
2018
Cited alongside, same era.

Then

Dong, X., Yang, Y.: Searching for a robust neural architecture in four gpu hours. In: CVPR (2019)
2019
Later among the works it cites.
Gu, J., Li, C., Zhang, B., Han, J., Cao, X., Liu, J., Doermann, D.: Projection convolutional neural networks for 1-bit cnns via discrete back propagation. In: AAAI (2019)
2019
Later among the works it cites.
Gu, J., Zhao, J., Jiang, X., Zhang, B., Liu, J., Guo, G., Ji, R.: Bayesian optimized 1-bit cnns. In: CVPR (2019)
2019
Later among the works it cites.
Li, L., Talwalkar, A.: Random search and reproducibility for neural architecture search. arXiv preprint arXiv:1902.07638 (2019)
Original
2019
Later among the works it cites.
Liu, C., Chen, L.C., Schroff, F., Adam, H., Hua, W., Yuille, A.L., Fei-Fei, L.: Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. In: CVPR (2019)

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2020

Learning Architectures for Binary Networks

Kim, Dahyun, Singh, Kunal Pratap, Choi, Jonghyun

Understand

Backbone architectures of most binary networks are well-known floating point architectures such as the ResNet family.

Questioning that the architectures designed for floating point networks would not be the best for binary networks, we propose to search architectures for binary networks (BNAS) by defining a new search space for binary architectures and a novel search objective.
Specifically, based on the cell based search method, we define the new search space of binary layer types, design a new cell template, and rediscover the utility of and propose to use the Zeroise layer instead of using it as a placeholder.
The novel search objective diversifies early search to learn better performing binary architectures.

Built on

Krizhevsky, A.: Learning Multiple Layers of Features from Tiny Images. Tech. rep. (2009)
2009
Earlier work this paper cites.
Jaderberg, M., Vedaldi, A., Zisserman, A.: Speeding up convolutional neural networks with low rank expansions. arXiv preprint arXiv:1405.3866 (2014)
Original
2014
Earlier work this paper cites.
Courbariaux, M., Bengio, Y., David, J.P.: Binaryconnect: Training deep neural networks with binary weights during propagations. In: NIPS (2015)
2015
Earlier work this paper cites.
Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: NIPS (2015)
2015
Earlier work this paper cites.
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)
Original
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.: Imagenet large scale visual recognition challenge. IJCV 115
2015
Earlier work this paper cites.
Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830 (2016)
Original
2016
Earlier work this paper cites.
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: Alexnet-level accuracy with 50x fewer parameters and < < 0.5mb model size. arXiv:1602.07360 (2016)
Original
2016
Earlier work this paper cites.
Li, F., Zhang, B., Liu, B.: Ternary weight networks. arXiv preprint arXiv:1605.04711 (2016)
Original
2016
Earlier work this paper cites.
Loshchilov, I., Hutter, F.: Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)
Original
2016
Earlier work this paper cites.
Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: Xnor-net: Imagenet classification using binary convolutional neural networks. In: ECCV (2016)
2016
Earlier work this paper cites.
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions (2016)
2016
Earlier work this paper cites.
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Original
2017
Earlier work this paper cites.
Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. In: ICLR (2017), https://arxiv.org/abs/1611.01144
Original
2017
Earlier work this paper cites.
Lin, X., Zhao, C., Pan, W.: Towards accurate binary convolutional neural network. In: NIPS (2017)
2017
Earlier work this paper cites.
Zoph, B., Le, Q.V.: Neural architecture search with reinforcement learning. In: ICLR (2017), https://openreview.net/forum?id=r1Ue8Hcxg
2017
Earlier work this paper cites.
Bender, G., Kindermans, P.J., Zoph, B., Vasudevan, V., Le, Q.: Understanding and simplifying one-shot architecture search. In: ICML (2018)
2018
Earlier work this paper cites.

Similar

Chen, Y., Meng, G., Zhang, Q., Zhang, X., Song, L., Xiang, S., Pan, C.: Joint neural architecture search and quantization. ArXiv abs/1811.09426
Original
2018
Cited alongside, same era.
Dong, J.D., Cheng, A.C., Juan, D.C., Wei, W., Sun, M.: Dpp-net: Device-aware progressive search for pareto-optimal neural architectures. In: ECCV (2018)
2018
Cited alongside, same era.
Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L.J., Fei-Fei, L., Yuille, A., Huang, J., Murphy, K.: Progressive neural architecture search. In: ECCV (2018)
2018
Cited alongside, same era.
Liu, H., Simonyan, K., Vinyals, O., Fernando, C., Kavukcuoglu, K.: Hierarchical representations for efficient architecture search. In: ICLR (2018), https://openreview.net/forum?id=BJQRKzbA-
2018
Cited alongside, same era.

Then

Dong, X., Yang, Y.: Searching for a robust neural architecture in four gpu hours. In: CVPR (2019)
2019
Later among the works it cites.
Gu, J., Li, C., Zhang, B., Han, J., Cao, X., Liu, J., Doermann, D.: Projection convolutional neural networks for 1-bit cnns via discrete back propagation. In: AAAI (2019)
2019
Later among the works it cites.
Gu, J., Zhao, J., Jiang, X., Zhang, B., Liu, J., Guo, G., Ji, R.: Bayesian optimized 1-bit cnns. In: CVPR (2019)
2019
Later among the works it cites.
Li, L., Talwalkar, A.: Random search and reproducibility for neural architecture search. arXiv preprint arXiv:1902.07638 (2019)
Original
2019
Later among the works it cites.
Liu, C., Chen, L.C., Schroff, F., Adam, H., Hua, W., Yuille, A.L., Fei-Fei, L.: Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. In: CVPR (2019)

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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Liu, Z., Wu, B., Luo, W., Yang, X., Liu, W., Cheng, K.T.: Bi-real net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm. In: ECCV (2018)

2018

Cited alongside, same era.

Luo, R., Tian, F., Qin, T., Chen, E., Liu, T.Y.: Neural architecture optimization. In: NIPS (2018)

2018

Cited alongside, same era.

Pham, H., Guan, M., Zoph, B., Le, Q., Dean, J.: Efficient neural architecture search via parameters sharing. In: ICML (2018)

2018

Cited alongside, same era.

Smith, L.N.: A disciplined approach to neural network hyper-parameters: Part 1–learning rate, batch size, momentum, and weight decay. arXiv preprint arXiv:1803.09820 (2018)

Original

2018

Cited alongside, same era.

Tan, S., Caruana, R., Hooker, G., Koch, P., Gordo, A.: Learning global additive explanations for neural nets using model distillation. arXiv preprint arXiv:1801.08640 (2018)

Original

2018

Cited alongside, same era.

Wu, B., Wang, Y., Zhang, P., Tian, Y., Vajda, P., Keutzer, K.: Mixed precision quantization of convnets via differentiable neural architecture search. ArXiv abs/1812.00090

Original

2018

Cited alongside, same era.

Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: CVPR (2018)

2018

Cited alongside, same era.

Zhou, Y., Ebrahimi, S., Arık, S.Ö., Yu, H., Liu, H., Diamos, G.: Resource-efficient neural architect. arXiv preprint arXiv:1806.07912 (2018)

Original

2018

Cited alongside, same era.

2019