NucNormZSL: điều chỉnh miền dựa trên chuẩn hạt nhân trong học không có dữ liệu

Neural Computing and Applications - Tập 34 - Trang 2353-2374 - 2021
Upendra Pratap Singh1, Krishna Pratap Singh1, Manoj Thakur2
1Indian Institute of Information Technology Allahabad, Prayagraj, India
2Indian Institute of Technology Mandi, Suran, India

Tóm tắt

Khả năng của con người trong việc nhận diện các khái niệm mới đã thu hút sự chú ý đáng kể từ cộng đồng nghiên cứu. Học không có dữ liệu, còn được gọi là học không có mẫu, tìm cách xây dựng các mô hình có thể nhận diện các trường hợp lớp mới ngay cả khi không "nhìn thấy" chúng trong quá trình huấn luyện; tuy nhiên, một số mô tả về các lớp mới là cần thiết. Trong công trình này, chúng tôi đặt vấn đề học không có mẫu như một bài toán học từ điển để tìm các hàm chiếu từ không gian đặc trưng sang không gian ngữ nghĩa như những từ điển trong các miền nguồn và đích. Để có được một ánh xạ chiếu mạnh mẽ trong miền nguồn, chúng tôi giới thiệu chuẩn hạt nhân để đạt được các giải pháp bậc thấp. Hơn nữa, từ điển có bậc thấp này được sử dụng như một phương pháp điều chỉnh trong miền đích để mà kiến thức chứa trong từ điển nguồn có thể được sử dụng trong miền đích. Trong các thí nghiệm của chúng tôi, miền nguồn chứa các hình ảnh của lớp đã thấy, các giá trị thực tế của chúng và các đại diện thuộc tính, trong khi dữ liệu tương ứng cho lớp chưa thấy được chứa trong miền đích. Chúng tôi cũng sử dụng sự lan truyền nhãn như một sự thay thế cho tìm kiếm hàng xóm gần nhất trong không gian ngữ nghĩa để gán nhãn lớp. Mô hình mà chúng tôi đề xuất, NucNormZSL, đạt được kết quả tốt nhất trong các bài kiểm tra trên bộ dữ liệu Các thuộc tính lớn (LAD) và duy trì sự cạnh tranh khá với các phương pháp hiện có trên bộ dữ liệu Động vật với Các thuộc tính-2 (AWA2), Chim Caltech-UCSD (CUB) và SUN trong cài đặt thông thường và cài đặt tổng quát.

Từ khóa

#học không có dữ liệu #chuẩn hạt nhân #học máy #điều chỉnh miền #ánh xạ chiếu

Tài liệu tham khảo

Akata Z, Perronnin F, Harchaoui Z, Schmid C (2015) Label-embedding for image classification. IEEE transLabel-embedding for image classification. IEEE transactions on pattern analysis and machine intelligence 38(7):1425-1438 Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Hasan M, Van Essen BC, Awwal AA, Asari VK (2019) A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3):292 Bhatia N et al (2010) Survey of nearest neighbor techniques. arXiv:1007.0085 Cao W, Zhou C, Wu Y, Ming Z, Xu Z, Zhang J (2020) Research progress of zero-shot learning beyond computer vision. In: International conference on algorithms and architectures for parallel processing, Springer, pp 538–551 Changpinyo S, Chao WL, Gong B, Sha F (2016) Synthesized classifiers for zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5327–5336 Chao WL, Changpinyo S, Gong B, Sha F (2016) An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. In: European conference on computer vision, Springer, pp 52–68 Elsken T, Staffler B, Metzen JH, Hutter F (2020) Meta-learning of neural architectures for few-shot learning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 12365–12375 Farhadi A, Endres I, Hoiem D, Forsyth D (2009) Describing objects by their attributes. In: 2009 IEEE conference on computer vision and pattern recognition, IEEE, pp 1778–1785 Fazel M, Hindi H, Boyd SP (2001) A rank minimization heuristic with application to minimum order system approximation. In: Proceedings of the 2001 American control conference (Cat. No. 01CH37148), IEEE, vol 6, pp 4734–4739 Fu Y, Hospedales TM, Xiang T, Fu Z, Gong S (2014) Transductive multi-view embedding for zero-shot recognition and annotation. In: European conference on computer vision, Springer, pp 584–599 Fu Y, Hospedales TM, Xiang T, Gong S (2015) Transductive multi-view zero-shot learning. IEEE Trans Pattern Anal Mach Intell 37(11):2332–2345 Fu Y, Xiang T, Jiang YG, Xue X, Sigal L, Gong S (2018) Recent advances in zero-shot recognition: toward data-efficient understanding of visual content. IEEE Signal Process Mag 35(1):112–125 Gopalan R, Li R, Chellappa R (2011) Domain adaptation for object recognition: an unsupervised approach. In: 2011 international conference on computer vision, IEEE, pp 999–1006 Hu Z, Nie F, Tian L, Wang R, Li X (2018) A comprehensive survey for low rank regularization. arXiv:1808.04521 Huang H, Wang C, Yu PS, Wang CD (2019) Generative dual adversarial network for generalized zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 801–810 Huynh D, Elhamifar E (2020) Fine-grained generalized zero-shot learning via dense attribute-based attention. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4483–4493 Jayaraman D, Grauman K (2014) Zero-shot recognition with unreliable attributes. In: Advances in neural information processing systems, pp 3464–3472 Ji Z, Cui B, Yu Y, Pang Y, Zhang Z (2021) Zero-shot classification with unseen prototype learning. In: Neural computing and applications, pp 1–11 Jiang H, Wang R, Shan S, Chen X (2019) Transferable contrastive network for generalized zero-shot learning. In: Proceedings of the IEEE international conference on computer vision, pp 9765–9774 Kadam S, Vaidya V (2018) Review and analysis of zero, one and few shot learning approaches. In: International conference on intelligent systems design and applications, Springer, pp 100–112 Kodirov E, Xiang T, Fu Z, Gong S (2015) Unsupervised domain adaptation for zero-shot learning. In: Proceedings of the IEEE international conference on computer vision, pp 2452–2460 Kumar Verma V, Arora G, Mishra A, Rai P (2018) Generalized zero-shot learning via synthesized examples. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4281–4289 Lampert CH, Nickisch H, Harmeling S (2013) Attribute-based classification for zero-shot visual object categorization. IEEE Trans Pattern Anal Mach Intell 36(3):453–465 Latif S, Rana R, Khalifa S, Jurdak R, Qadir J, Schuller BW (2020) Deep representation learning in speech processing: challenges, recent advances, and future trends. arXiv:2001.00378 Li X, Zhang D, Ye M, Li X, Dou Q, Lv Q (2020) Bidirectional generative transductive zero-shot learning. In: Neural computing and applications, pp 1–14 Li Y, Wang D, Hu H, Lin Y, Zhuang Y (2017) Zero-shot recognition using dual visual-semantic mapping paths. arXiv:1703.05002 Liu J, Kuipers B, Savarese S (2011) Recognizing human actions by attributes. In: CVPR 2011, IEEE, pp 3337–3344 Newman M (2005) Power laws, pareto distributions and Zipf’s law. Contemp Phys 46(5):323–351. https://doi.org/10.1080/00107510500052444 Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359 Parikh D, Grauman K (2011) Relative attributes. In: 2011 International conference on computer vision, IEEE, pp 503–510 Rahman S, Khan S, Porikli F (2018) A unified approach for conventional zero-shot, generalized zero-shot, and few-shot learning. IEEE Trans Image Process 27(11):5652–5667 Recht B, Fazel M, Parrilo PA (2010) Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM Rev 52(3):471–501 Ribani R, Marengoni M (2019) A survey of transfer learning for convolutional neural networks. In: 2019 32nd SIBGRAPI conference on graphics, patterns and images tutorials (SIBGRAPI-T), IEEE, pp 47–57 Ruder S, Peters ME, Swayamdipta S, Wolf T (2019) Transfer learning in natural language processing. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: tutorials, pp 15–18 Schonfeld E, Ebrahimi S, Sinha S, Darrell T, Akata Z (2019) Generalized zero-and few-shot learning via aligned variational autoencoders. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8247–8255 Sen PC, Hajra M, Ghosh M (2020) Supervised classification algorithms in machine learning: a survey and review. In: Emerging technology in modelling and graphics, Springer, pp 99–111 Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 Soni AN (2018) Application and analysis of transfer learning-survey. Int J Sci Res Eng Dev 1(2):272–278 Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C (2018) A survey on deep transfer learning. In: International conference on artificial neural networks, Springer, pp 270–279 van Wieringen WN (2015) Lecture notes on ridge regression. arXiv:1509.09169 Wang Q, Chen K (2017) Zero-shot visual recognition via bidirectional latent embedding. Int J Comput Vis 124(3):356–383 Wang W, Zheng VW, Yu H, Miao C (2019) A survey of zero-shot learning: settings, methods, and applications. ACM Trans Intell Syst Technol (TIST) 10(2):1–37 Wang Y, Yao Q, Kwok JT, Ni LM (2020) Generalizing from a few examples: a survey on few-shot learning. ACM Comput Surv (CSUR) 53(3):1–34 Wu X, Sahoo D, Hoi SC (2020) Recent advances in deep learning for object detection. Neurocomputing 396:39–64 Xian Y, Schiele B, Akata Z (2017) Zero-shot learning-the good, the bad and the ugly. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4582–4591 Xian Y, Lampert CH, Schiele B, Akata Z (2018) Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans Pattern Anal Mach Intell 41(9):2251–2265 Xiao J, Hays J, Ehinger KA, Oliva A, Torralba A (2010) Sun database: large-scale scene recognition from abbey to zoo. In: 2010 IEEE computer society conference on computer vision and pattern recognition, IEEE, pp 3485–3492 Xiao J, Hays J, Ehinger KA, Oliva A, Torralba A (2010) Sun database: large-scale scene recognition from abbey to zoo. In: 2010 IEEE computer society conference on computer vision and pattern recognition, IEEE, pp 3485–3492 Xu X, Hospedales T, Gong S (2017) Transductive zero-shot action recognition by word-vector embedding. Int J Comput Vis 123(3):309–333 Xu X, Hospedales T, Gong S (2017) Transductive zero-shot action recognition by word-vector embedding. Int J Comput Vis 123(3):309–333 Xu X, Shen F, Yang Y, Zhang D, Tao Shen H, Song J (2017) Matrix tri-factorization with manifold regularizations for zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3798–3807 Ye M, Guo Y (2017) Zero-shot classification with discriminative semantic representation learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7140–7148 Zha Z, Wen B, Zhang J, Zhou J, Zhu C (2019) A comparative study for the nuclear norms minimization methods. In: 2019 IEEE international conference on image processing (ICIP), IEEE, pp 2050–2054 Zhang J, Li W, Ogunbona P, Xu D (2019) Recent advances in transfer learning for cross-dataset visual recognition: a problem-oriented perspective. ACM Comput Surv (CSUR) 52(1):1–38 Zhang L, Xiang T, Gong S (2017) Learning a deep embedding model for zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2021–2030 Zhao B, Fu Y, Liang R, Wu J, Wang Y, Wang Y (2019) A large-scale attribute dataset for zero-shot learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops Zhu P, Wang H, Saligrama V (2019) Generalized zero-shot recognition based on visually semantic embedding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2995–3003 Zhu X, Ghahramani Z (2002) Learning from labeled and unlabeled data with label propagation Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q (2020) A comprehensive survey on transfer learning. In: Proceedings of the IEEE