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Erschienen in: International Journal of Computer Vision 8-9/2020

21.01.2020

Learning an Evolutionary Embedding via Massive Knowledge Distillation

verfasst von: Xiang Wu, Ran He, Yibo Hu, Zhenan Sun

Erschienen in: International Journal of Computer Vision | Ausgabe 8-9/2020

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Abstract

Knowledge distillation methods aim at transferring knowledge from a large powerful teacher network to a small compact student one. These methods often focus on close-set classification problems and matching features between teacher and student networks from a single sample. However, many real-world classification problems are open-set. This paper proposes an Evolutionary Embedding Learning (EEL) framework to learn a fast and accurate student network for open-set problems via massive knowledge distillation. First, we revisit the formulation of canonical knowledge distillation and make it suitable for the open-set problems with massive classes. Second, by introducing an angular constraint, a novel correlated embedding loss (CEL) is proposed to match embedding spaces between the teacher and student network from a global perspective. Lastly, we propose a simple yet effective paradigm towards a fast and accurate student network development for knowledge distillation. We show the possibility to implement an accelerated student network without sacrificing accuracy, compared with its teacher network. The experimental results are quite encouraging. EEL achieves better performance with other state-of-the-art methods for various large-scale open-set problems, including face recognition, vehicle re-identification and person re-identification.

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Literatur
Zurück zum Zitat Ba, J., & Caruana, R. (2014). Do deep nets really need to be deep? In NeurIPS. Ba, J., & Caruana, R. (2014). Do deep nets really need to be deep? In NeurIPS.
Zurück zum Zitat Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., & Shah, R. (1994). Signature verification using a“ siamese” time delay neural network. In NeurIPS. Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., & Shah, R. (1994). Signature verification using a“ siamese” time delay neural network. In NeurIPS.
Zurück zum Zitat Chen, Y., Wang, N., & Zhang, Z. (2018). Darkrank: Accelerating deep metric learning via cross sample similarities transfer. In AAAI. Chen, Y., Wang, N., & Zhang, Z. (2018). Darkrank: Accelerating deep metric learning via cross sample similarities transfer. In AAAI.
Zurück zum Zitat Chen, J., Yi, D., Yang, J., Zhao, G., Li, S.Z., & Pietikainen, M. (2009) Learning mappings for face synthesis from near infrared to visual light images. In CVPR. Chen, J., Yi, D., Yang, J., Zhao, G., Li, S.Z., & Pietikainen, M. (2009) Learning mappings for face synthesis from near infrared to visual light images. In CVPR.
Zurück zum Zitat Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016). Binarized neural networks: Training deep neural networks with weights and activations constrained to + 1 or - 1. In NeurIPS. Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., & Bengio, Y. (2016). Binarized neural networks: Training deep neural networks with weights and activations constrained to + 1 or - 1. In NeurIPS.
Zurück zum Zitat Czarnecki, W. M., Osindero, S., Jaderberg, M., Swirszcz, G., & Pascanu, R. (2017). Sobolev training for neural networks. In NeurIPS. Czarnecki, W. M., Osindero, S., Jaderberg, M., Swirszcz, G., & Pascanu, R. (2017). Sobolev training for neural networks. In NeurIPS.
Zurück zum Zitat Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In CVPR. Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In CVPR.
Zurück zum Zitat Ding, S., Lin, L., Wang, G., & Chao, H. Y. (2015). Deep feature learning with relative distance comparison for person re-identification. Pattern Recognition, 48, 2993.CrossRef Ding, S., Lin, L., Wang, G., & Chao, H. Y. (2015). Deep feature learning with relative distance comparison for person re-identification. Pattern Recognition, 48, 2993.CrossRef
Zurück zum Zitat Guo, Y., Zhang, L., Hu, Y., He, X., & Gao, J. (2016). Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In ECCV. Guo, Y., Zhang, L., Hu, Y., He, X., & Gao, J. (2016). Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In ECCV.
Zurück zum Zitat Guo, H., Zhao, C., Liu, Z., Wang, J., & Lu, H. (2018). Learning coarse-to-fine structured feature embedding for vehicle re-identification. In AAAI. Guo, H., Zhao, C., Liu, Z., Wang, J., & Lu, H. (2018). Learning coarse-to-fine structured feature embedding for vehicle re-identification. In AAAI.
Zurück zum Zitat Han, S., Mao, H., & Dally, W. J. (2016). Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. In ICLR. Han, S., Mao, H., & Dally, W. J. (2016). Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. In ICLR.
Zurück zum Zitat Han, S., Pool, J., Tran, J., & Dally, W. J. (2015). Learning both weights and connections for efficient neural network. In NeurIPS. Han, S., Pool, J., Tran, J., & Dally, W. J. (2015). Learning both weights and connections for efficient neural network. In NeurIPS.
Zurück zum Zitat He, R., Wu, X., Sun, Z., & Tan, T. (2017). Learning invariant deep representation for nir-vis face recognition. In AAAI. He, R., Wu, X., Sun, Z., & Tan, T. (2017). Learning invariant deep representation for nir-vis face recognition. In AAAI.
Zurück zum Zitat He, R., Wu, X., Sun, Z., & Tan, T. (2018). Wasserstein CNN: Learning invariant features for NIR-VIS face recognition. In TPAMI. He, R., Wu, X., Sun, Z., & Tan, T. (2018). Wasserstein CNN: Learning invariant features for NIR-VIS face recognition. In TPAMI.
Zurück zum Zitat He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In CVPR. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In CVPR.
Zurück zum Zitat Hinton, G. E., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. In NeurIPS workshop. Hinton, G. E., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. In NeurIPS workshop.
Zurück zum Zitat Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., et al. (2017). Mobilenets: Efficientconvolutional neural networks for mobile vision applications. CoRR arXiv:1704.04861. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., et al. (2017). Mobilenets: Efficientconvolutional neural networks for mobile vision applications. CoRR arXiv:​1704.​04861.
Zurück zum Zitat Huang, C., Loy, C. C., & Tang, X. (2016). Local similarity-aware deep feature embedding. In NeurIPS. Huang, C., Loy, C. C., & Tang, X. (2016). Local similarity-aware deep feature embedding. In NeurIPS.
Zurück zum Zitat Huang, G. B., Ramesh, M., Berg, T., & Learned-Miller, E. (2007). Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst. Huang, G. B., Ramesh, M., Berg, T., & Learned-Miller, E. (2007). Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07-49, University of Massachusetts, Amherst.
Zurück zum Zitat Huang, D., Sun, J., & Wang, Y. (2012). The BUAA-VisNir face database instructions. Technical Report IRIP-TR-12-FR-001, Beihang University, Beijing, China Huang, D., Sun, J., & Wang, Y. (2012). The BUAA-VisNir face database instructions. Technical Report IRIP-TR-12-FR-001, Beihang University, Beijing, China
Zurück zum Zitat Iandola, F. N., Moskewicz, M. W., Ashraf, K., Han, S., Dally, W. J., & Keutzer, K. (2016). Squeezenet: Alexnet-level accuracy with 50x fewer parameters and\(<\)1 mb model size. CoRR arXiv:1602.07360. Iandola, F. N., Moskewicz, M. W., Ashraf, K., Han, S., Dally, W. J., & Keutzer, K. (2016). Squeezenet: Alexnet-level accuracy with 50x fewer parameters and\(<\)1 mb model size. CoRR arXiv:​1602.​07360.
Zurück zum Zitat Kemelmacher-Shlizerman, I., Seitz, S. M., Miller, D., & Brossard, E. (2016). The megaface benchmark: 1 million faces for recognition at scale. In CVPR. Kemelmacher-Shlizerman, I., Seitz, S. M., Miller, D., & Brossard, E. (2016). The megaface benchmark: 1 million faces for recognition at scale. In CVPR.
Zurück zum Zitat Kim, J., Park, S., & Kwak, N. (2018). Paraphrasing complex network: Network compression via factor transfer. In NeurIPS. Kim, J., Park, S., & Kwak, N. (2018). Paraphrasing complex network: Network compression via factor transfer. In NeurIPS.
Zurück zum Zitat Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. In ICLR. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. In ICLR.
Zurück zum Zitat Li, H., Kadav, A., Durdanovic, I., Samet, H., & Graf, H. P. (2017). Pruning filters for efficient convnets. In: ICLR. Li, H., Kadav, A., Durdanovic, I., Samet, H., & Graf, H. P. (2017). Pruning filters for efficient convnets. In: ICLR.
Zurück zum Zitat Li, S. Z., Yi, D., Lei, Z., & Liao, S. (2013). The casia nir-vis 2.0 face database. In CVPR workshops. Li, S. Z., Yi, D., Lei, Z., & Liao, S. (2013). The casia nir-vis 2.0 face database. In CVPR workshops.
Zurück zum Zitat Liao, S., Lei, Z., Yi, D., Li, & S. Z. (2014). A benchmark study of large-scale unconstrained face recognition. In IJCB. Liao, S., Lei, Z., Yi, D., Li, & S. Z. (2014). A benchmark study of large-scale unconstrained face recognition. In IJCB.
Zurück zum Zitat Liu, Y., Cao, J., Li, B., Yuan, Y., Hu, W., Li, Y., & Duan, Y. (2019). Knowledge distillation via instance relationship graph. In CVPR. Liu, Y., Cao, J., Li, B., Yuan, Y., Hu, W., Li, Y., & Duan, Y. (2019). Knowledge distillation via instance relationship graph. In CVPR.
Zurück zum Zitat Liu, X., Liu, W., Ma, H., & Fu, H. (2016). Large-scale vehicle re-identification in urban surveillance videos. In ICME. Liu, X., Liu, W., Ma, H., & Fu, H. (2016). Large-scale vehicle re-identification in urban surveillance videos. In ICME.
Zurück zum Zitat Liu, X., Song, L., Wu, X., & Tan, T. (2016). Transferring deep representation for nir-vis heterogeneous face recognition. In ICB. Liu, X., Song, L., Wu, X., & Tan, T. (2016). Transferring deep representation for nir-vis heterogeneous face recognition. In ICB.
Zurück zum Zitat Liu, H., Tian, Y., Wang, Y., Pang, L., & Huang, T. (2016). Deep relative distance learning: Tell the difference between similar vehicles. In CVPR. Liu, H., Tian, Y., Wang, Y., Pang, L., & Huang, T. (2016). Deep relative distance learning: Tell the difference between similar vehicles. In CVPR.
Zurück zum Zitat Liu, W., Wen, Y., Yu, Z., & Yang, M. (2016). Large-margin softmax loss for convolutional neural networks. In ICML. Liu, W., Wen, Y., Yu, Z., & Yang, M. (2016). Large-margin softmax loss for convolutional neural networks. In ICML.
Zurück zum Zitat Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L. (2017). Sphereface: Deep hypersphere embedding for face recognition. In CVPR. Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., & Song, L. (2017). Sphereface: Deep hypersphere embedding for face recognition. In CVPR.
Zurück zum Zitat Liu, X., Liu, W., Mei, T., & Ma, H. (2018). Provid: Progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Transactions on Multimedia, 20, 645–658.CrossRef Liu, X., Liu, W., Mei, T., & Ma, H. (2018). Provid: Progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Transactions on Multimedia, 20, 645–658.CrossRef
Zurück zum Zitat Luo, H., Gu, Y., Liao, X., Lai, S., & Jiang, W. (2019). Bag of tricks and a strong baseline for deep person re-identification. In CVPR Workshops. Luo, H., Gu, Y., Liao, X., Lai, S., & Jiang, W. (2019). Bag of tricks and a strong baseline for deep person re-identification. In CVPR Workshops.
Zurück zum Zitat Luo, J. H., Wu, J., & Lin, W. (2017). Thinet: A filter level pruning method for deep neural network compression. In ICCV. Luo, J. H., Wu, J., & Lin, W. (2017). Thinet: A filter level pruning method for deep neural network compression. In ICCV.
Zurück zum Zitat Luo, P., Zhu, Z., Liu, Z., Wang, X., & Tang, X. (2016). Face model compression by distilling knowledge from neurons. In AAAI. Luo, P., Zhu, Z., Liu, Z., Wang, X., & Tang, X. (2016). Face model compression by distilling knowledge from neurons. In AAAI.
Zurück zum Zitat Molchanov, P., Tyree, S., Karras, T., Aila, T., & Kautz, J. (2017). Pruning convolutional neural networks for resource efficient inference. In ICLR. Molchanov, P., Tyree, S., Karras, T., Aila, T., & Kautz, J. (2017). Pruning convolutional neural networks for resource efficient inference. In ICLR.
Zurück zum Zitat Ng, H., & Winkler, S. (2014). A data-driven approach to cleaning large face datasets. In ICIP. Ng, H., & Winkler, S. (2014). A data-driven approach to cleaning large face datasets. In ICIP.
Zurück zum Zitat Park, W., Kim, D., Lu, Y., & Cho, M. (2019). Relational knowledge distillation. In CVPR. Park, W., Kim, D., Lu, Y., & Cho, M. (2019). Relational knowledge distillation. In CVPR.
Zurück zum Zitat Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). Deep face recognition. In BMVC. Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). Deep face recognition. In BMVC.
Zurück zum Zitat Passalis, N., & Tefas, A. (2018). Learning deep representations with probabilistic knowledge transfer. In ECCV. Passalis, N., & Tefas, A. (2018). Learning deep representations with probabilistic knowledge transfer. In ECCV.
Zurück zum Zitat Ranjan, R., Castillo, C. D., & Chellappa, R. (2017). L2-constrained softmax loss for discriminative face verification. CoRR arXiv:1703.09507. Ranjan, R., Castillo, C. D., & Chellappa, R. (2017). L2-constrained softmax loss for discriminative face verification. CoRR arXiv:​1703.​09507.
Zurück zum Zitat Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016). Xnor-net: Imagenet classification using binary convolutional neural networks. In ECCV. Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016). Xnor-net: Imagenet classification using binary convolutional neural networks. In ECCV.
Zurück zum Zitat Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016). Performance measures and a data set for multi-target, multi-camera tracking. In ECCV workshop. Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016). Performance measures and a data set for multi-target, multi-camera tracking. In ECCV workshop.
Zurück zum Zitat Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., & Bengio, Y. (2015). Fitnets: Hints for thin deep nets. In ICLR. Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., & Bengio, Y. (2015). Fitnets: Hints for thin deep nets. In ICLR.
Zurück zum Zitat Sandler, M., Howard, A. G., Zhu, M., Zhmoginov, A., & Chen, L. (2018). Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. CoRR arXiv:1801.04381. Sandler, M., Howard, A. G., Zhu, M., Zhmoginov, A., & Chen, L. (2018). Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. CoRR arXiv:​1801.​04381.
Zurück zum Zitat Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In CVPR. Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In CVPR.
Zurück zum Zitat Sohn, K. (2016). Improved deep metric learning with multi-class n-pair loss objective. In NeurIPS. Sohn, K. (2016). Improved deep metric learning with multi-class n-pair loss objective. In NeurIPS.
Zurück zum Zitat Song, C., Huang, Y., Ouyang, W., & Wang, L. (2018). Mask-guided contrastive attention model for person re-identification. In CVPR. Song, C., Huang, Y., Ouyang, W., & Wang, L. (2018). Mask-guided contrastive attention model for person re-identification. In CVPR.
Zurück zum Zitat Song, H. O., Jegelka, S., Rathod, V., & Murphy, K. (2017). Deep metric learning via facility location. In CVPR. Song, H. O., Jegelka, S., Rathod, V., & Murphy, K. (2017). Deep metric learning via facility location. In CVPR.
Zurück zum Zitat Song, H.O., Xiang, Y., Jegelka, S., & Savarese, S. (2016). Deep metric learning via lifted structured feature embedding. In CVPR. Song, H.O., Xiang, Y., Jegelka, S., & Savarese, S. (2016). Deep metric learning via lifted structured feature embedding. In CVPR.
Zurück zum Zitat Sun, Y., Chen, Y., Wang, X., & Tang, X. (2014). Deep learning face representation by joint identification-verification. In NeurIPS. Sun, Y., Chen, Y., Wang, X., & Tang, X. (2014). Deep learning face representation by joint identification-verification. In NeurIPS.
Zurück zum Zitat Sun, Y., Wang, X., & Tang, X. (2015). Deeply learned face representations are sparse, selective, and robust. In CVPR. Sun, Y., Wang, X., & Tang, X. (2015). Deeply learned face representations are sparse, selective, and robust. In CVPR.
Zurück zum Zitat Sun, Y., Zheng, L., Deng, W., & Wang, S. (2017). Svdnet for pedestrian retrieval. In ICCV. Sun, Y., Zheng, L., Deng, W., & Wang, S. (2017). Svdnet for pedestrian retrieval. In ICCV.
Zurück zum Zitat Wang, F., Xiang, X., Cheng, J., & Yuille, A. L. (2017). Normface: \(\text{L}_{2}\) hypersphere embedding for face verification. In ACM MM. Wang, F., Xiang, X., Cheng, J., & Yuille, A. L. (2017). Normface: \(\text{L}_{2}\) hypersphere embedding for face verification. In ACM MM.
Zurück zum Zitat Wang, C., Zhang, Q., Huang, C., Liu, W., & Wang, X. (2018). Mancs: A multi-task attentional network with curriculum sampling for person re-identification. In: ECCV. Wang, C., Zhang, Q., Huang, C., Liu, W., & Wang, X. (2018). Mancs: A multi-task attentional network with curriculum sampling for person re-identification. In: ECCV.
Zurück zum Zitat Wang, J., Zhou, F., Wen, S., Liu, X., & Lin, Y. (2017). Deep metric learning with angular loss. In ICCV. Wang, J., Zhou, F., Wen, S., Liu, X., & Lin, Y. (2017). Deep metric learning with angular loss. In ICCV.
Zurück zum Zitat Wen, Y., Zhang, K., Li, Z., & Qiao, Y. (2016). A discriminative feature learning approach for deep face recognition. In ECCV. Wen, Y., Zhang, K., Li, Z., & Qiao, Y. (2016). A discriminative feature learning approach for deep face recognition. In ECCV.
Zurück zum Zitat Wu, X., Song, L., He, R., & Tan, T. (2018). Coupled deep learning for heterogeneous face recognition. In AAAI. Wu, X., Song, L., He, R., & Tan, T. (2018). Coupled deep learning for heterogeneous face recognition. In AAAI.
Zurück zum Zitat Wu, X., He, R., Sun, Z., & Tan, T. (2018). A light CNN for deep face representation with noisy labels. IEEE Transactions on Information Forensics and Security, 13, 2884–2896.CrossRef Wu, X., He, R., Sun, Z., & Tan, T. (2018). A light CNN for deep face representation with noisy labels. IEEE Transactions on Information Forensics and Security, 13, 2884–2896.CrossRef
Zurück zum Zitat Yim, J., Joo, D., Bae, J., & Kim, J. (2017). A gift from knowledge distillation: Fast optimization, network minimization and transfer learning. In CVPR. Yim, J., Joo, D., Bae, J., & Kim, J. (2017). A gift from knowledge distillation: Fast optimization, network minimization and transfer learning. In CVPR.
Zurück zum Zitat Yuan, Y., Yang, K., & Zhang, C. (2017). Hard-aware deeply cascaded embedding. In ICCV. Yuan, Y., Yang, K., & Zhang, C. (2017). Hard-aware deeply cascaded embedding. In ICCV.
Zurück zum Zitat Zagoruyko, S., & Komodakis, N. (2017). Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. In ICLR. Zagoruyko, S., & Komodakis, N. (2017). Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. In ICLR.
Zurück zum Zitat Zhang, Z., Lan, C., Zeng, W., & Chen, Z. (2019). Densely semantically aligned person re-identification. In CVPR. Zhang, Z., Lan, C., Zeng, W., & Chen, Z. (2019). Densely semantically aligned person re-identification. In CVPR.
Zurück zum Zitat Zhang, Y., Xiang, T., Hospedales, T. M., & Lu, H. (2018). Deep mutual learning. In: CVPR. Zhang, Y., Xiang, T., Hospedales, T. M., & Lu, H. (2018). Deep mutual learning. In: CVPR.
Zurück zum Zitat Zhang, X., Zhou, X., Lin, M., & Sun, J. (2017). Shufflenet: An extremely efficient convolutional neural network for mobile devices. CoRR arXiv:1707.01083. Zhang, X., Zhou, X., Lin, M., & Sun, J. (2017). Shufflenet: An extremely efficient convolutional neural network for mobile devices. CoRR arXiv:​1707.​01083.
Zurück zum Zitat Zhang, R., Lin, L., Zhang, R., Zuo, W., & Zhang, L. (2015). Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Transactions on Image Processing, 24, 4766–4779.MathSciNetCrossRef Zhang, R., Lin, L., Zhang, R., Zuo, W., & Zhang, L. (2015). Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Transactions on Image Processing, 24, 4766–4779.MathSciNetCrossRef
Zurück zum Zitat Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015). Scalable person re-identification: A benchmark. In ICCV. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., & Tian, Q. (2015). Scalable person re-identification: A benchmark. In ICCV.
Zurück zum Zitat Zheng, Z., Yang, X., Yu, Z., Zheng, L., Yang, Y., & Kautz, J. (2019). Joint discriminative and generative learning for person re-identification. In CVPR. Zheng, Z., Yang, X., Yu, Z., Zheng, L., Yang, Y., & Kautz, J. (2019). Joint discriminative and generative learning for person re-identification. In CVPR.
Zurück zum Zitat Zheng, Z., Zheng, L., & Yang, Y. (2017). Pedestrian alignment network for large-scale person re-identification. CoRR arXiv:1707.00408. Zheng, Z., Zheng, L., & Yang, Y. (2017). Pedestrian alignment network for large-scale person re-identification. CoRR arXiv:​1707.​00408.
Zurück zum Zitat Zheng, Z., Zheng, L., & Yang, Y. (2017). Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In ICCV. Zheng, Z., Zheng, L., & Yang, Y. (2017). Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In ICCV.
Zurück zum Zitat Zhong, Z., Zheng, L., Zheng, Z., Li, S., & Yang, Y. (2018). Camera style adaptation for person re-identification. In CVPR. Zhong, Z., Zheng, L., Zheng, Z., Li, S., & Yang, Y. (2018). Camera style adaptation for person re-identification. In CVPR.
Zurück zum Zitat Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2017). Learning transferable architectures for scalable image recognition. CoRR arXiv:1707.07012. Zoph, B., Vasudevan, V., Shlens, J., & Le, Q. V. (2017). Learning transferable architectures for scalable image recognition. CoRR arXiv:​1707.​07012.
Metadaten
Titel
Learning an Evolutionary Embedding via Massive Knowledge Distillation
verfasst von
Xiang Wu
Ran He
Yibo Hu
Zhenan Sun
Publikationsdatum
21.01.2020
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 8-9/2020
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-019-01286-x

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