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Recent works show that reducing the number of layers in a convolutional neural network can enhance efficiency while maintaining the performance of the network.
Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2014
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Fast r-cnn
Girshick, R · 2015
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Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., and Dally, W · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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Group normalization
Wu, Y. and He, K · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Pruning convolutional neural networks for resource efficient inference
Molchanov, P., Tyree, S., Karras, T., Aila, T., and Kautz, J · 2016
Earlier work this paper cites.
Efficient inference with tensorrt
Vanholder, H · 2016
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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
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Pruning filters for efficient convnets
Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H. P · 2017
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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
Earlier work this paper cites.
Shallowing deep networks: Layer-wise pruning based on feature representations
Chen, S. and Zhao, Q · 2018
Cited alongside, same era.
Amc: Automl for model compression and acceleration on mobile devices
He, Y., Lin, J., Liu, Z., Wang, H., Li, L.-J., and Han, S · 2018
Cited alongside, same era.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Cited alongside, same era.
Proxylessnas: Direct neural architecture search on target task and hardware, 2019
Cai, H., Zhu, L., and Han, S · 2019
Cited alongside, same era.
Metapruning: Meta learning for automatic neural network channel pruning
Liu, Z., Mu, H., Zhang, X., Guo, Z., Yang, X., Cheng, K., and Sun, J · 2019
Cited alongside, same era.
Importance estimation for neural network pruning
Molchanov, P., Mallya, A., Tyree, S., Frosio, I., and Kautz, J · 2019
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2021
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Resnet strikes back: An improved training procedure in timm
Wightman, R., Touvron, H., and Jégou, H · 2021
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Layer folding: Neural network depth reduction using activation linearization
Dror, A. B., Zehngut, N., Raviv, A., Artyomov, E., Vitek, R., and Jevnisek, R · 2022
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Spdy: Accurate pruning with speedup guarantees
Frantar, E. and Alistarh, D · 2022
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Depthshrinker: A new compression paradigm towards boosting real-hardware efficiency of compact neural networks
Fu, Y., Yang, H., Yuan, J., Li, M., Wan, C., Krishnamoorthi, R., Chandra, V., and Lin, Y · 2022
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Fairgrape: Fairness-aware gradient pruning method for face attribute classification
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Cited alongside, same era.
Knapsack pruning with inner distillation
Aflalo, Y., Noy, A., Lin, M., Friedman, I., and Zelnik, L · 2020
Cited alongside, same era.
To filter prune, or to layer prune, that is the question
Elkerdawy, S., Elhoushi, M., Singh, A., Zhang, H., and Ray, N · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Characterising bias in compressed models
Hooker, S., Moorosi, N., Clark, G., Bengio, S., and Denton, E · 2020
Cited alongside, same era.
Discriminative layer pruning for convolutional neural networks
Jordao, A., Lie, M., and Schwartz, W. R · 2020
Cited alongside, same era.
Prune responsibly
Paganini, M · 2020
Cited alongside, same era.
Lin, X.-Z., Kim, S., and Joo, J · 2022
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A convnet for the 2020s
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S · 2022
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Structural pruning via latency-saliency knapsack
Shen, M., Yin, H., Molchanov, P., Mao, L., Liu, J., and Alvarez, J · 2022
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Pruning has a disparate impact on model accuracy
Tran, C., Fioretto, F., Kim, J.-E., and Naidu, R · 2022
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Fast as chita: Neural network pruning with combinatorial optimization
Benbaki, R., Chen, W., Meng, X., Hazimeh, H., Ponomareva, N., Zhao, Z., and Mazumder, R · 2023
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Structural pruning for diffusion models
Fang, G., Ma, X., and Wang, X · 2023
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SparseGPT: Massive language models can be accurately pruned in one-shot
Frantar, E. and Alistarh, D · 2023
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Efficient latency-aware cnn depth compression via two-stage dynamic programming
Kim, J., Jeong, Y., Lee, D., and Song, H. O · 2023
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Balancing act: Constraining disparate impact in sparse models
Hashemizadeh, M., Ramirez, J., Sukumaran, R., Farnadi, G., Lacoste-Julien, S., and Gallego-Posada, J · 2024
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