Abstract
Online knowledge distillation has attracted increasing interest recently, which jointly learns teacher and student models or an ensemble of student models simultaneously and collaboratively. On the other hand, existing works focus more on outcome-driven learning according to knowledge like classification probabilities whereas the distilling processes which capture rich and useful intermediate features and information are largely neglected. In this work, we propose an innovative adversarial-based mutual learning network (AMLN) that introduces process-driven learning beyond outcome-driven learning for augmented online knowledge distillation. A block-wise training module is designed which guides the information flow and mutual learning among peer networks adversarially throughout different learning stages, and this spreads until the final network layer which captures more high-level information. AMLN has been evaluated under a variety of network architectures over three widely used benchmark datasets. Extensive experiments show that AMLN achieves superior performance consistently against state-of-the-art knowledge transfer methods.
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References
Alex Krizhevsky, V.N., Hinton, G.: Cifar-10 (Canadian institute for advanced research)
Alex Krizhevsky, V.N., Hinton, G.: Cifar-100 (Canadian institute for advanced research)
Anil, R., Pereyra, G., Passos, A., Ormandi, R., Dahl, G.E., Hinton, G.E.: Large scale distributed neural network training through online distillation. arXiv preprint arXiv:1804.03235 (2018)
Ba, L.J., Caruana, R.: Do deep nets really need to be deep? In: Advances in Neural Information Processing Systems, pp. 2654–2662 (2013)
Bucilu, C., Caruana, R., Niculescu-Mizil, A.: Model compression. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 535–541 (2006)
Bulat, A., Tzimiropoulos, G.: Binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources. In: IEEE International Conference on Computer Vision, pp. 3706–3714 (2017)
Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply supervised nets. In: Artificial Intelligence and Statistics, pp. 562–570 (2015)
Chen, T., Goodfellow, I., Shlens, J.: Net2net: accelerating learning via knowledge transfer. In: International Conference on Learning Representations (2016)
Courbariaux, M., Hubara, I., Soudry, D., Ran, E.Y., Bengio, Y.: Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or \(-\)1. arXiv preprint arXiv:1602.02830 (2016)
Dai, J., He, K., Sun, J.: Instance-aware semantic segmentation via multi-task network cascades. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3150–3158 (2016)
Felzenszwalb, P.F., Girshick, R.B., Mcallester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 1627–1645 (2010)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2014)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (2014)
Han, S., Pool, J., Tran, J., Dally, W.J.: Learning both weights and connections for efficient neural networks. In: Advances in Neural Information Processing Systems, pp. 1135–1143 (2015)
Hao, L., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. arXiv preprint arXiv:1608.08710 (2016)
He, K., Zhang, X., Ren, S., Jian, S.: Deep residual learning for image recognition, pp. 770–778 (2016)
He, Y., Zhang, X., Jian, S.: Channel pruning for accelerating very deep neural networks. In: IEEE International Conference on Computer Vision, pp. 1389–1397 (2017)
Heo, B., Lee, M., Yun, S., Choi, J.Y.: Knowledge transfer via distillation of activation boundaries formed by hidden neurons. In: Proceedings of AAAI Conference on Artificial Intelligence, pp. 3779–3787 (2019)
Howard, A.G., et al.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and \(<\)0.5 mb model size. arXiv preprint arXiv:1602.07360 (2016)
Luo, J.-H., Wu, J., Lin, W.: Thinet: a filter level pruning method for deep neural network compression. In: IEEE International Conference on Computer Vision, pp. 5058–5066 (2017)
Kim, J., Hyun, M., Chung, I., Kwak, N.: Feature fusion for online mutual knowledge distillation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2019)
Kim, J., Park, S., Kwak, N.: Paraphrasing complex network: network compression via factor transfer. In: Advances in Neural Information Processing Systems, pp. 2760–2769 (2018)
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Lan, X., Zhu, X., Gong, S.: Knowledge distillation by on-the-fly native ensemble. In: Advances in Neural Information Processing Systems, pp. 7528–7538 (2018)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Molchanov, P., Tyree, S., Karras, T., Aila, T., Kautz, J.: Pruning convolutional neural networks for resource efficient transfer learning. arXiv preprint arXiv:1611.06440 (2016)
Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-Net: imagenet classification using binary convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 525–542. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_32
Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Bengio, Y.: Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 211–252 (2015)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of IEEE International Conference on Computer Vision, pp. 618–626 (2019)
Song, G., Chai, W.: Collaborative learning for deep neural networks. In: Advances in Neural Information Processing Systems, pp. 1837–1846 (2018)
Zagoruyko, S., Komodakis, N.: Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928 (2016)
Zagoruyko, S., Komodakis, N.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)
Zhang, X., Gong, H., Dai, X., Yang, F., Liu, N., Liu, M.: Understanding pictograph with facial features: end-to-end sentence-level lip reading of Chinese. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 9211–9218 (2019)
Zhang, Y., Xiang, T., Hospedales, T.M., Lu, H.: Deep mutual learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4320–4328 (2018)
Acknowledgements
This work is supported in part by National Science Foundation of China under Grant No. 61572113, and the Fundamental Research Funds for the Central Universities under Grants No. XGBDFZ09.
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Zhang, X., Lu, S., Gong, H., Luo, Z., Liu, M. (2020). AMLN: Adversarial-Based Mutual Learning Network for Online Knowledge Distillation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12357. Springer, Cham. https://doi.org/10.1007/978-3-030-58610-2_10
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