Learning to Model the Tail
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Martial Hebert | Deva Ramanan | Yu-Xiong Wang | M. Hebert | D. Ramanan | Yu-Xiong Wang | Deva Ramanan
[1] Andrew Y. Ng,et al. Zero-Shot Learning Through Cross-Modal Transfer , 2013, NIPS.
[2] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[3] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[4] Sanja Fidler,et al. Predicting Deep Zero-Shot Convolutional Neural Networks Using Textual Descriptions , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[5] Martial Hebert,et al. Growing a Brain: Fine-Tuning by Increasing Model Capacity , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Shaogang Gong,et al. Recent Advances in Zero-Shot Recognition: Toward Data-Efficient Understanding of Visual Content , 2018, IEEE Signal Processing Magazine.
[7] Bolei Zhou,et al. Places: A 10 Million Image Database for Scene Recognition , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] Jürgen Schmidhuber,et al. Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement , 1997, Machine Learning.
[9] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[10] Hugo Larochelle,et al. Optimization as a Model for Few-Shot Learning , 2016, ICLR.
[11] Marcin Andrychowicz,et al. Learning to learn by gradient descent by gradient descent , 2016, NIPS.
[12] Joshua B. Tenenbaum,et al. Human-level concept learning through probabilistic program induction , 2015, Science.
[13] Xiaogang Wang,et al. Factors in Finetuning Deep Model for Object Detection with Long-Tail Distribution , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[15] Bhaskara Marthi,et al. A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs , 2017, Science.
[16] Oriol Vinyals,et al. Matching Networks for One Shot Learning , 2016, NIPS.
[17] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[18] Luca Bertinetto,et al. Learning feed-forward one-shot learners , 2016, NIPS.
[19] J. Urgen Schmidhuber. Learning to Control Fast-weight Memories: an Alternative to Dynamic Recurrent Networks , 1991 .
[20] Sebastian Thrun,et al. Learning to Learn , 1998, Springer US.
[21] Shaogang Gong,et al. Recent Advances in Zero-Shot Recognition: Toward Data-Efficient Understanding of Visual Content , 2017, IEEE Signal Processing Magazine.
[22] Alexei A. Efros,et al. What makes ImageNet good for transfer learning? , 2016, ArXiv.
[23] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[24] Rich Caruana,et al. Multitask Learning , 1997, Machine-mediated learning.
[25] Quoc V. Le,et al. HyperNetworks , 2016, ICLR.
[26] Trevor Darrell,et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.
[27] Jitendra Malik,et al. Analyzing the Performance of Multilayer Neural Networks for Object Recognition , 2014, ECCV.
[28] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[29] Andrea Vedaldi,et al. Learning multiple visual domains with residual adapters , 2017, NIPS.
[30] Gregory R. Koch,et al. Siamese Neural Networks for One-Shot Image Recognition , 2015 .
[31] Chen Huang,et al. Learning Deep Representation for Imbalanced Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Martial Hebert,et al. Learning by Transferring from Unsupervised Universal Sources , 2016, AAAI.
[33] Derek Hoiem,et al. Learning without Forgetting , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[34] J. Schmidhuber,et al. A neural network that embeds its own meta-levels , 1993, IEEE International Conference on Neural Networks.
[35] Pietro Perona,et al. The Devil is in the Tails: Fine-grained Classification in the Wild , 2017, ArXiv.
[36] Martial Hebert,et al. Model recommendation: Generating object detectors from few samples , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Dragomir Anguelov,et al. Capturing Long-Tail Distributions of Object Subcategories , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[38] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Jitendra Malik,et al. Learning to Optimize , 2016, ICLR.
[40] Daan Wierstra,et al. One-shot Learning with Memory-Augmented Neural Networks , 2016, ArXiv.
[41] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[42] Martial Hebert,et al. Learning to Learn: Model Regression Networks for Easy Small Sample Learning , 2016, ECCV.
[43] Qingming Huang,et al. Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks , 2015, ECCV.
[44] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[45] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Mausoom Sarkar,et al. Introspection: Accelerating Neural Network Training By Learning Weight Evolution , 2017, ICLR.
[47] Charless C. Fowlkes,et al. Do We Need More Training Data? , 2015, International Journal of Computer Vision.
[48] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[49] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[50] Krista A. Ehinger,et al. SUN Database: Exploring a Large Collection of Scene Categories , 2014, International Journal of Computer Vision.
[51] Hong Yu,et al. Meta Networks , 2017, ICML.
[52] Michael S. Bernstein,et al. Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations , 2016, International Journal of Computer Vision.
[53] Bharath Hariharan,et al. Low-Shot Visual Recognition by Shrinking and Hallucinating Features , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[54] Samy Bengio. Sharing Representations for Long Tail Computer Vision Problems , 2015, ICMI.
[55] Yann LeCun,et al. Signature Verification Using A "Siamese" Time Delay Neural Network , 1993, Int. J. Pattern Recognit. Artif. Intell..
[56] Richard S. Zemel,et al. Prototypical Networks for Few-shot Learning , 2017, NIPS.
[57] Raquel Urtasun,et al. Few-Shot Learning Through an Information Retrieval Lens , 2017, NIPS.
[58] Martial Hebert,et al. Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs , 2016, NIPS.
[59] Bohyung Han,et al. Image Question Answering Using Convolutional Neural Network with Dynamic Parameter Prediction , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[60] Pietro Perona,et al. One-shot learning of object categories , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[61] Chen Sun,et al. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[62] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.