Bayesian Zero-Shot Learning
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[1] Andrew Zisserman,et al. Automated Flower Classification over a Large Number of Classes , 2008, 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing.
[2] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[3] Murat Dundar,et al. Batch discovery of recurring rare classes toward identifying anomalous samples , 2014, KDD.
[4] Padhraic Smyth,et al. Hierarchical Dirichlet Processes with Random Effects , 2006, NIPS.
[5] Tao Xiang,et al. Learning to Compare: Relation Network for Few-Shot Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[6] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[7] Ali Farhadi,et al. Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[8] Samy Bengio,et al. Zero-Shot Learning by Convex Combination of Semantic Embeddings , 2013, ICLR.
[9] Joshua B. Tenenbaum,et al. One-Shot Learning with a Hierarchical Nonparametric Bayesian Model , 2011, ICML Unsupervised and Transfer Learning.
[10] Geoffrey E. Hinton,et al. Zero-shot Learning with Semantic Output Codes , 2009, NIPS.
[11] Shaogang Gong,et al. Semantic Autoencoder for Zero-Shot Learning , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Bernt Schiele,et al. Latent Embeddings for Zero-Shot Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Andrew Y. Ng,et al. Zero-Shot Learning Through Cross-Modal Transfer , 2013, NIPS.
[15] James Hays,et al. SUN attribute database: Discovering, annotating, and recognizing scene attributes , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[16] Venkatesh Saligrama,et al. Zero-Shot Learning via Semantic Similarity Embedding , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[17] Marc'Aurelio Ranzato,et al. DeViSE: A Deep Visual-Semantic Embedding Model , 2013, NIPS.
[18] Bernt Schiele,et al. Evaluation of output embeddings for fine-grained image classification , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[20] Piyush Rai,et al. A Simple Exponential Family Framework for Zero-Shot Learning , 2017, ECML/PKDD.
[21] 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.
[22] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Christoph H. Lampert,et al. Attribute-Based Classification for Zero-Shot Visual Object Categorization , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[24] 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.
[25] Timothy M. Hospedales,et al. Gaussian Visual-Linguistic Embedding for Zero-Shot Recognition , 2016, EMNLP.
[26] Murat Dundar,et al. The Infinite Mixture of Infinite Gaussian Mixtures , 2014, NIPS.
[27] Wei-Lun Chao,et al. Synthesized Classifiers for Zero-Shot Learning , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Murat Dundar,et al. A non-parametric Bayesian model for joint cell clustering and cluster matching: identification of anomalous sample phenotypes with random effects , 2014, BMC Bioinformatics.
[29] Philip H. S. Torr,et al. An embarrassingly simple approach to zero-shot learning , 2015, ICML.
[30] Anderson Rocha,et al. Toward Open Set Recognition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.