Fetching the paper…
Reading the bibliography…
Knowledge distillation, a technique recently gaining popularity for enhancing model generalization in Convolutional Neural Networks (CNNs), operates under the assumption that both teacher and student models are trained on identical data distributions.
Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y., 2014 · 2014
Earlier work this paper cites.
Exploiting linear structure within convolutional networks for efficient evaluation, in: Advances in Neural Information Processing Systems, pp. 1269–1277
Denton, E.L., Zaremba, W., Bruna, J., LeCun, Y., Fergus, R., 2014 · 2014
Earlier work this paper cites.
Generative adversarial networks, in: Advances in Neural Information Processing Systems, pp. 2672–2680
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., 2014 · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mirza, M., Osindero, S., 2014 · 2014
Earlier work this paper cites.
Feature-map-level online adversarial knowledge distillation, in: Proceedings of the International Conference on Machine Learning, PMLR. pp. 2006–2015
Chung, I., Park, S., Kim, J., Kwak, N., 2020 · 2015
Earlier work this paper cites.
Deep convolutional networks on graph-structured data
Henaff, M., Bruna, J., LeCun, Y., 2015 · 2015
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., Dean, J., 2015 · 2015
Earlier work this paper cites.
Fitnets: Hints for thin deep nets, in: Proceedings of the International Conference on Learning Representations
Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y., 2015 · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition, in: Proceedings of the International Conference on Learning Representations
Simonyan, K., Zisserman, A., 2015 · 2015
Earlier work this paper cites.
Going deeper with convolutions, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015 · 2015
Earlier work this paper cites.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P., 2016 · 2016
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., Vandergheynst, P., 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Earlier work this paper cites.
Quantized convolutional neural networks for mobile devices, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4820–4828
Wu, J., Leng, C., Wang, Y., Hu, Q., Cheng, J., 2016 · 2016
Earlier work this paper cites.
Revisiting semi-supervised learning with graph embeddings, in: Proceedings of the International Conference on Machine Learning, PMLR. pp. 40–48
Yang, Z., Cohen, W., Salakhudinov, R., 2016 · 2016
Earlier work this paper cites.
Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A., 2017 · 2017
Cited alongside, same era.
Inductive representation learning on large graphs
Hamilton, W.L., Ying, R., Leskovec, J., 2017 · 2017
Cited alongside, same era.
Mask R-CNN, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988
He, K., Gkioxari, G., Dollár, P., Girshick, R.B., 2017 · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T.N., Welling, M., 2017 · 2017
Cited alongside, same era.
Least squares generative adversarial networks, in: Proceedings of the IEEE International Conference on Computer Vision, pp. 2794–2802
Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S., 2017 · 2017
Cited alongside, same era.
Kdgan: Knowledge distillation with generative adversarial networks., in: Advances in Neural Information Processing Systems, pp. 783–794
Wang, X., Zhang, R., Sun, Y., Qi, J., 2018 · 2018
Later among the works it cites.
Deep mutual learning, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4320–4328
Zhang, Y., Xiang, T., Hospedales, T.M., Lu, H., 2018 · 2018
Later among the works it cites.
Relational knowledge distillation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3967–3976
Park, W., Kim, D., Lu, Y., Cho, M., 2019 · 2019
Later among the works it cites.
Dynamic graph CNN for learning on point clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M., 2019 · 2019
Later among the works it cites.
How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., Jegelka, S., 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Qi, C.R., Su, H., Mo, K., Guibas, L.J., 2017 · 2017
Cited alongside, same era.
Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
Zagoruyko, S., Komodakis, N., 2017 · 2017
Cited alongside, same era.
Predicting multicellular function through multi-layer tissue networks
Zitnik, M., Leskovec, J., 2017 · 2017
Cited alongside, same era.
Netgan: Generating graphs via random walks, in: Proceedings of the International Conference on Machine Learning, PMLR. pp. 610–619
Bojchevski, A., Shchur, O., Zügner, D., Günnemann, S., 2018 · 2018
Cited alongside, same era.
Molgan: An implicit generative model for small molecular graphs
De Cao, N., Kipf, T., 2018 · 2018
Cited alongside, same era.
Knowledge distillation by on-the-fly native ensemble, in: Advances in Neural Information Processing Systems, pp. 7528–7538
Lan, X., Zhu, X., Gong, S., 2018 · 2018
Cited alongside, same era.
Large-scale point cloud semantic segmentation with superpoint graphs, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4558–4567
Landrieu, L., Simonovsky, M., 2018 · 2018
Cited alongside, same era.
Online knowledge distillation with diverse peers, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 3430–3437
Chen, D., Mei, J.P., Wang, C., Feng, Y., Chen, C., 2020 · 2020
Later among the works it cites.
Graph convolutional networks for hyperspectral image classification
Hong, D., Gao, L., Yao, J., Zhang, B., Plaza, A., Chanussot, J., 2020 · 2020
Later among the works it cites.
Contrastive representation distillation
Tian, Y., Krishnan, D., Isola, P., 2020 · 2020
Later among the works it cites.
Distilling knowledge from graph convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7074–7083
Yang, Y., Qiu, J., Song, M., Tao, D., Wang, X., 2020 · 2020
Later among the works it cites.
Knowledge distillation with distribution mismatch, in: Machine Learning and Knowledge Discovery in Databases. Research Track: European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part II 21, Springer. pp. 250–265
Nguyen, D., Gupta, S., Nguyen, T., Rana, S., Nguyen, P., Tran, T., Le, K., Ryan, S., Venkatesh, S., 2021 · 2021
Closest in time.
Extract the knowledge of graph neural networks and go beyond it: An effective knowledge distillation framework, in: Proceedings of the Web Conference 2021, pp. 1227–1237
Yang, C., Liu, J., Shi, C., 2021 · 2021
Closest in time.
Knowledge distillation with the reused teacher classifier, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 11933–11942
Chen, D., Mei, J.P., Zhang, H., Wang, C., Feng, Y., Chen, C., 2022 · 2022
Closest in time.
Teacher–student knowledge distillation based on decomposed deep feature representation for intelligent mobile applications
Sepahvand, M., Abdali-Mohammadi, F., Taherkordi, A., 2022 · 2022
Closest in time.
Exploring the combination of self and mutual teaching for tabular-data-related semi-supervised regression
Zhang, Y.L., Zhou, J., Shi, Q., Li, L., 2022 · 2022
Closest in time.