Fetching the paper…
Reading the bibliography…
This technical report describes Johns Hopkins University speaker recognition system submitted to Voxceleb Speaker Recognition Challenge 2021 Track 3: Self-supervised speaker verification (closed).
2014
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2016
2016
Earlier work this paper cites.
W.-N. Hsu, Y. Zhang, and J. Glass, “Unsupervised learning of disentangled and interpretable representations from sequential data,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , 2017, pp. 1876–1887
2017
Earlier work this paper cites.
J. S. Chung, A. Nagrani, and A. Zisserman, “Voxceleb2: Deep speaker recognition,” in INTERSPEECH , 2018
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 4690–4699
2019
Earlier work this paper cites.
S. Gao, M.-M. Cheng, K. Zhao, X.-Y. Zhang, M.-H. Yang, and P. H. Torr, “Res2net: A new multi-scale backbone architecture,” IEEE transactions on pattern analysis and machine intelligence , 2019
2019
Earlier work this paper cites.
A. Nagrani, J. S. Chung, W. Xie, and A. Zisserman, “Voxceleb: Large-scale speaker verification in the wild,” Comput. Speech Lang. , vol. 60, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Z. Peng, S. Feng, and T. Lee, “Mixture factorized auto-encoder for unsupervised hierarchical deep factorization of speech signal,” in ICASSP 2020 . IEEE, 2020, pp. 6774–6778
2020
Cited alongside, same era.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 9729–9738
2020
Later among the works it cites.
J. Deng, J. Guo, T. Liu, M. Gong, and S. Zafeiriou, “Sub-center arcface: Boosting face recognition by large-scale noisy web faces,” in European Conference on Computer Vision . Springer, 2020, pp. 741–757
2020
Later among the works it cites.
J. Villalba, N. Chen, D. Snyder, D. Garcia-Romero, A. McCree, G. Sell, J. Borgstrom, L. P. García-Perera, F. Richardson, R. Dehak, P. A. Torres-Carrasquillo, and N. Dehak, “State-of-the-art speaker recognition with neural network embeddings in nist sre18 and speakers in the wild evaluations,” Comput. Speech Lang. , vol. 60, 2020
2020
Later among the works it cites.
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in Proceedings of the International Conference on Computer Vision (ICCV) , 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, B. Piot, k. kavukcuoglu, R. Munos, and M. Valko, “Bootstrap your own latent - a new approach to self-supervised learning,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 21 271–21 284
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Closest in time.
W. Xia, C. Zhang, C. Weng, M. Yu, and D. Yu, “Self-supervised text-independent speaker verification using prototypical momentum contrastive learning,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 6723–6727
2021
Closest in time.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=YicbFdNTTy
2021
Closest in time.