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The tradeoff between performance and inference speed is critical for practical applications.
Acnet: Strengthening the kernel skeletons for powerful cnn via asymmetric convolution blocks
Ding, X.; Guo, Y.; Ding, G.; and Han, J. 2019 · 1920
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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Rectified linear units improve restricted boltzmann machines
Nair, V.; and Hinton, G. E. 2010 · 2010
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Speech recognition with deep recurrent neural networks
Graves, A.; Mohamed, A.-r.; and Hinton, G. 2013 · 2013
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Rectifier nonlinearities improve neural network acoustic models
Maas, A. L.; Hannun, A. Y.; Ng, A. Y.; et al. 2013 · 2013
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Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
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Deep learning with limited numerical precision
Gupta, S.; Agrawal, A.; Gopalakrishnan, K.; and Narayanan, P. 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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Fully convolutional networks for semantic segmentation
Long, J.; Shelhamer, E.; and Darrell, T. 2015 · 2015
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The cityscapes dataset for semantic urban scene understanding
Cordts, M.; Omran, M.; Ramos, S.; Rehfeld, T.; Enzweiler, M.; Benenson, R.; Franke, U.; Roth, S.; and Schiele, B. 2016 · 2016
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Analysis of the quantization error in digital multipliers with small wordlength
Dehner, G.; Dehner, I.; Rabenstein, R.; Schäfer, M.; and Strobl, C. 2016 · 2016
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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You only look once: Unified, real-time object detection
Redmon, J.; Divvala, S.; Girshick, R.; and Farhadi, A. 2016 · 2016
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.-C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; and Yuille, A. L. 2017 · 2017
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Mask r-cnn
He, K.; Gkioxari, G.; Dollár, P.; and Girshick, R. 2017 · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; and Adam, H. 2017 · 2017
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Diracnets: Training very deep neural networks without skip-connections
Zagoruyko, S.; and Komodakis, N. 2017 · 2017
Cited alongside, same era.
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C.; Zhu, Y.; Papandreou, G.; Schroff, F.; and Adam, H. 2018 · 2018
Cited alongside, same era.
Ristretto: A framework for empirical study of resource-efficient inference in convolutional neural networks
Gysel, P.; Pimentel, J.; Motamedi, M.; and Ghiasi, S. 2018 · 2018
Cited alongside, same era.
Quantization and training of neural networks for efficient integer-arithmetic-only inference
Jacob, B.; Kligys, S.; Chen, B.; Zhu, M.; Tang, M.; Howard, A.; Adam, H.; and Kalenichenko, D. 2018 · 2018
Cited alongside, same era.
Zeroq: A novel zero shot quantization framework
Cai, Y.; Yao, Z.; Dong, Z.; Gholami, A.; Mahoney, M. W.; and Keutzer, K. 2020 · 2020
Later among the works it cites.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2020 · 2020
Later among the works it cites.
Repvgg: Making vgg-style convnets great again
Ding, X.; Zhang, X.; Ma, N.; Han, J.; Ding, G.; and Sun, J. 2021 · 2021
Later among the works it cites.
HPTQ: Hardware-Friendly Post Training Quantization
Habi, H. V.; Peretz, R.; Cohen, E.; Dikstein, L.; Dror, O.; Diamant, I.; Jennings, R. H.; and Netzer, A. 2021 · 2021
Later among the works it cites.
Do All MobileNets Quantize Poorly? Gaining Insights into the Effect of Quantization on Depthwise Separable Convolutional Networks Through the Eyes of Multi-scale Distributional Dynamics
Yun, S.; and Wong, A. 2021 · 2021
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Krishnamoorthi, R. 2018 · 2018
Cited alongside, same era.
TensorRT
NVIDIA. 2018 · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; and Chen, L.-C. 2018 · 2018
Cited alongside, same era.
A quantization-friendly separable convolution for mobilenets
Sheng, T.; Feng, C.; Zhuo, S.; Zhang, X.; Shen, L.; and Aleksic, M. 2018 · 2018
Cited alongside, same era.
Mixed precision quantization of convnets via differentiable neural architecture search
Wu, B.; Wang, Y.; Zhang, P.; Tian, Y.; Vajda, P.; and Keutzer, K. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
Cited alongside, same era.
Searching for mobilenetv3
Howard, A.; Sandler, M.; Chu, G.; Chen, L.-C.; Chen, B.; Tan, M.; Wang, W.; Zhu, Y.; Pang, R.; Vasudevan, V.; et al. 2019 · 2019
Cited alongside, same era.
Later among the works it cites.
Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices
Zhang, X.; Zeng, H.; and Zhang, L. 2021 · 2021
Later among the works it cites.
Scaling up your kernels to 31x31: Revisiting large kernel design in cnns
Ding, X.; Zhang, X.; Han, J.; and Ding, G. 2022 · 2022
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Online Convolutional Re-parameterization
Hu, M.; Feng, J.; Hua, J.; Lai, B.; Huang, J.; Gong, X.; and Hua, X.-S. 2022 · 2022
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Efficient and accurate quantized image super-resolution on mobile NPUs, mobile AI & AIM 2022 challenge: report
Ignatov, A.; Timofte, R.; Denna, M.; Younes, A.; Gankhuyag, G.; Huh, J.; Kim, M. K.; Yoon, K.; Moon, H.-C.; Lee, S.; et al. 2023 · 2022
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YOLOv6: a single-stage object detection framework for industrial applications
Li, C.; Li, L.; Jiang, H.; Weng, K.; Geng, Y.; Li, L.; Ke, Z.; Li, Q.; Cheng, M.; Nie, W.; Li, Y.; Zhang, B.; Liang, Y.; Zhou, L.; Xu, X.; Chu, X.; Wei, X.; and Wei, X. 2022 · 2022
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An Improved One millisecond Mobile Backbone
Vasu, P. K. A.; Gabriel, J.; Zhu, J.; Tuzel, O.; and Ranjan, A. 2022 · 2022
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YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
Wang, C.-Y.; Bochkovskiy, A.; and Liao, H.-Y. M. 2022 · 2022
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MemSR: Training Memory-efficient Lightweight Model for Image Super-Resolution
Wu, K.; Lee, C.-K.; and Ma, K. 2022 · 2022
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PP-YOLOE: An evolved version of YOLO
Xu, S.; Wang, X.; Lv, W.; Chang, Q.; Cui, C.; Deng, K.; Wang, G.; Dang, Q.; Wei, S.; Du, Y.; et al. 2022 · 2022
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Re-parameterizing Your Optimizers rather than Architectures
Ding, X.; Chen, H.; Zhang, X.; Huang, K.; Han, J.; and Ding, G. 2023 · 2023
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FastPillars: A Deployment-friendly Pillar-based 3D Detector
Zhou, S.; Tian, Z.; Chu, X.; Zhang, X.; Zhang, B.; Lu, X.; Feng, C.; Jie, Z.; Chiang, P. Y.; and Ma, L. 2023 · 2023
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