Dual VAEGAN: A generative model for generalized zero-shot learning

[1]  Ling Shao,et al.  Region Graph Embedding Network for Zero-Shot Learning , 2020, ECCV.

[2]  Sethuraman Panchanathan,et al.  Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learning , 2020, ECCV.

[3]  Ling Shao,et al.  A Joint Label Space for Generalized Zero-Shot Classification , 2020, IEEE Transactions on Image Processing.

[4]  Xizhao Wang,et al.  Erratum to "Entropy-based fuzzy support vector machine for imbalanced datasets" [Knowl.-Based Syst. 115 (2017) 87-99] , 2020, Knowl. Based Syst..

[5]  Farhad Pourpanah,et al.  Recent advances in deep learning , 2020, International Journal of Machine Learning and Cybernetics.

[6]  Ling Shao,et al.  Zero-VAE-GAN: Generating Unseen Features for Generalized and Transductive Zero-Shot Learning , 2020, IEEE Transactions on Image Processing.

[7]  Michel Crucianu,et al.  Modeling Inter and Intra-Class Relations in the Triplet Loss for Zero-Shot Learning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[8]  Zi Huang,et al.  Alleviating Feature Confusion for Generative Zero-shot Learning , 2019, ACM Multimedia.

[9]  Shiguang Shan,et al.  Transferable Contrastive Network for Generalized Zero-Shot Learning , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[10]  Jian Ni,et al.  Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning , 2019, NeurIPS.

[11]  Nanning Zheng,et al.  Compressing Unknown Images With Product Quantizer for Efficient Zero-Shot Classification , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Fei Zhang,et al.  Co-Representation Network for Generalized Zero-Shot Learning , 2019, ICML.

[13]  Zi Huang,et al.  Leveraging the Invariant Side of Generative Zero-Shot Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Debasmit Das,et al.  Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[15]  Bernt Schiele,et al.  F-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Gal Chechik,et al.  Adaptive Confidence Smoothing for Generalized Zero-Shot Learning , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Trevor Darrell,et al.  Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Philip S. Yu,et al.  Generative Dual Adversarial Network for Generalized Zero-Shot Learning , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Xirong Li,et al.  Dissimilarity Representation Learning for Generalized Zero-Shot Recognition , 2018, ACM Multimedia.

[20]  Li Liu,et al.  A Joint Generative Model for Zero-Shot Learning , 2018, ECCV Workshops.

[21]  Gustavo Carneiro,et al.  Multi-modal Cycle-consistent Generalized Zero-Shot Learning , 2018, ECCV.

[22]  Wei Liu,et al.  Zero-Shot Visual Recognition Using Semantics-Preserving Adversarial Embedding Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[23]  Xi Peng,et al.  A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[24]  Bernt Schiele,et al.  Feature Generating Networks for Zero-Shot Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[25]  Renato A. Krohling,et al.  Restricted Boltzmann machine to determine the input weights for extreme learning machines , 2017, Expert Syst. Appl..

[26]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[27]  Christoph H. Lampert,et al.  Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Nuno Vasconcelos,et al.  Semantically Consistent Regularization for Zero-Shot Recognition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Aaron C. Courville,et al.  Improved Training of Wasserstein GANs , 2017, NIPS.

[30]  Ruslan Salakhutdinov,et al.  Learning Robust Visual-Semantic Embeddings , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[31]  Tao Xiang,et al.  Learning a Deep Embedding Model for Zero-Shot Learning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Bernt Schiele,et al.  Learning Deep Representations of Fine-Grained Visual Descriptions , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Wei-Lun Chao,et al.  An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild , 2016, ECCV.

[34]  Ole Winther,et al.  Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.

[35]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Honglak Lee,et al.  Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.

[37]  Philip H. S. Torr,et al.  An embarrassingly simple approach to zero-shot learning , 2015, ICML.

[38]  F. Perronnin,et al.  Label-Embedding for Image Classification , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Timothy M. Hospedales,et al.  Transductive Multi-View Zero-Shot Learning , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Aaron C. Courville,et al.  Generative adversarial networks , 2014, Commun. ACM.

[41]  Angeliki Lazaridou,et al.  Is this a wampimuk? Cross-modal mapping between distributional semantics and the visual world , 2014, ACL.

[42]  Marc'Aurelio Ranzato,et al.  DeViSE: A Deep Visual-Semantic Embedding Model , 2013, NIPS.

[43]  Andrew Y. Ng,et al.  Zero-Shot Learning Through Cross-Modal Transfer , 2013, NIPS.

[44]  James Hays,et al.  SUN attribute database: Discovering, annotating, and recognizing scene attributes , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[45]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[46]  Ali Farhadi,et al.  Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[47]  Christoph H. Lampert,et al.  Learning to detect unseen object classes by between-class attribute transfer , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[48]  Andrew Zisserman,et al.  Automated Flower Classification over a Large Number of Classes , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.

[49]  Herna L. Viktor,et al.  Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach , 2004, SKDD.

[50]  M. Friedman A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings , 1940 .

[51]  Claudia-Adina Dragos,et al.  Combined Model-Free Adaptive Control with Fuzzy Component by Virtual Reference Feedback Tuning for Tower Crane Systems , 2019, ITQM.

[52]  Michael I. Jordan,et al.  Generalized Zero-Shot Learning with Deep Calibration Network , 2018, NeurIPS.

[53]  Supplementary materials for: CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training , 2017 .

[54]  R. Iman,et al.  Approximations of the critical region of the fbietkan statistic , 1980 .

[55]  Christoph H. Lampert,et al.  Attribute-Based Classification for Zero-Shot Visual Object Categorization , 2022 .