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How can we bring both privacy and energy-efficiency to a neural system? In this paper, we propose PrivateSNN, which aims to build low-power Spiking Neural Networks (SNNs) from a pre-trained ANN model without leaking sensitive information contained in a dataset.
Spiking-yolo: Spiking neural network for real-time object detection
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Revisit knowledge distillation: a teacher-free framework
Yuan, L.; Tay, F. E.; Li, G.; Wang, T.; and Feng, J. 2019 · 1909
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Dropout: a simple way to prevent neural networks from overfitting
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Sharmin, S.; Rathi, N.; Panda, P.; and Roy, K. 2020 · 2003
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Learning multiple layers of features from tiny images
Krizhevsky, A.; and Hinton, G. 2009 · 2009
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Going deeper with directly-trained larger spiking neural networks
Zheng, H.; Wu, Y.; Deng, L.; Hu, Y.; and Li, G. 2020 · 2011
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The spinnaker project
Furber, S. B.; Galluppi, F.; Temple, S.; and Plana, L. A. 2014 · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
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Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip
Akopyan, F.; Sawada, J.; Cassidy, A.; Alvarez-Icaza, R.; Arthur, J.; Merolla, P.; Imam, N.; Nakamura, Y.; Datta, P.; Nam, G.-J.; et al. 2015 · 2015
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Unsupervised learning of digit recognition using spike-timing-dependent plasticity
Diehl, P. U.; and Cook, M. 2015 · 2015
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Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Diehl, P. U.; Neil, D.; Binas, J.; Cook, M.; Liu, S.-C.; and Pfeiffer, M. 2015 · 2015
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Distilling the knowledge in a neural network
Hinton, G.; Vinyals, O.; and Dean, J. 2015 · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
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Understanding neural networks through deep visualization
Yosinski, J.; Clune, J.; Nguyen, A.; Fuchs, T.; and Lipson, H. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
Training deep spiking neural networks using backpropagation
Lee, J. H.; Delbruck, T.; and Pfeiffer, M. 2016 · 2016
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2017 · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch
Paszke, A.; Gross, S.; Chintala, S.; Chanan, G.; Yang, E.; DeVito, Z.; Lin, Z.; Desmaison, A.; Antiga, L.; and Lerer, A. 2017 · 2017
Cited alongside, same era.
Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Rueckauer, B.; Lungu, I.-A.; Hu, Y.; Pfeiffer, M.; and Liu, S.-C. 2017 · 2017
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Sparse computation in adaptive spiking neural networks
Zambrano, D.; Nusselder, R.; Scholte, H. S.; and Bohté, S. M. 2019 · 2019
Later among the works it cites.
Neuromorphic Nearest Neighbor Search Using Intel’s Pohoiki Springs
Frady, E. P.; Orchard, G.; Florey, D.; Imam, N.; Liu, R.; Mishra, J.; Tse, J.; Wild, A.; Sommer, F. T.; and Davies, M. 2020 · 2020
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Deep Spiking Neural Network: Energy Efficiency Through Time based Coding
Han, B.; and Roy, K. 2020 · 2020
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RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network
Han, B.; et al. 2020 · 2020
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The knowledge within: Methods for data-free model compression
Haroush, M.; Hubara, I.; Hoffer, E.; and Soudry, D. 2020 · 2020
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Towards Privacy-Preserving Domain Adaptation
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Loihi: A neuromorphic manycore processor with on-chip learning
Davies, M.; Srinivasa, N.; Lin, T.-H.; Chinya, G.; Cao, Y.; Choday, S. H.; Dimou, G.; Joshi, P.; Imam, N.; Jain, S.; et al. 2018 · 2018
Cited alongside, same era.
Spectral normalization for generative adversarial networks
Miyato, T.; Kataoka, T.; Koyama, M.; and Yoshida, Y. 2018 · 2018
Cited alongside, same era.
cGANs with projection discriminator
Miyato, T.; and Koyama, M. 2018 · 2018
Cited alongside, same era.
Ask, acquire, and attack: Data-free uap generation using class impressions
Mopuri, K. R.; Uppala, P. K.; and Babu, R. V. 2018 · 2018
Cited alongside, same era.
Spatio-temporal backpropagation for training high-performance spiking neural networks
Wu, Y.; Deng, L.; Li, G.; Zhu, J.; and Shi, L. 2018 · 2018
Cited alongside, same era.
A swarm optimization solver based on ferroelectric spiking neural networks
Fang, Y.; Wang, Z.; Gomez, J.; Datta, S.; Khan, A. I.; and Raychowdhury, A. 2019 · 2019
Cited alongside, same era.
Zero-shot knowledge distillation in deep networks
Nayak, G. K.; Mopuri, K. R.; Shaj, V.; Radhakrishnan, V. B.; and Chakraborty, A. 2019 · 2019
Cited alongside, same era.
Kim, Y.; Cho, D.; and Hong, S. 2020 · 2020
Later among the works it cites.
Revisiting batch normalization for training low-latency deep spiking neural networks from scratch
Kim, Y.; and Panda, P. 2020 · 2020
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Universal source-free domain adaptation
Kundu, J. N.; Venkat, N.; Babu, R. V.; et al. 2020 · 2020
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Enabling spike-based backpropagation for training deep neural network architectures
Lee, C.; Sarwar, S. S.; Panda, P.; Srinivasan, G.; and Roy, K. 2020 · 2020
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Liang, J.; Hu, D.; and Feng, J. 2020 · 2020
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Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connections, stochastic softmax, and hybridization
Panda, P.; Aketi, S. A.; and Roy, K. 2020 · 2020
Later among the works it cites.
Federated learning with spiking neural networks
Venkatesha, Y.; Kim, Y.; Tassiulas, L.; and Panda, P. 2021 · 2021
Closest in time.
2022 roadmap on neuromorphic computing and engineering
Christensen, D. V.; Dittmann, R.; Linares-Barranco, B.; Sebastian, A.; Le Gallo, M.; Redaelli, A.; Slesazeck, S.; Mikolajick, T.; Spiga, S.; Menzel, S.; et al. 2022 · 2022
Closest in time.
Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting
Deng, S.; Li, Y.; Zhang, S.; and Gu, S. 2022 · 2022
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