Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition
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[1] Razvan Pascanu,et al. Meta-Learning with Latent Embedding Optimization , 2018, ICLR.
[2] Tao Xiang,et al. Learning to Compare: Relation Network for Few-Shot Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[3] Sergey Levine,et al. Probabilistic Model-Agnostic Meta-Learning , 2018, NeurIPS.
[4] Li Fei-Fei,et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.
[5] Bharath Hariharan,et al. Low-Shot Visual Recognition by Shrinking and Hallucinating Features , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[6] Jeff Donahue,et al. Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.
[7] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[8] Hugo Larochelle,et al. Optimization as a Model for Few-Shot Learning , 2016, ICLR.
[9] Alexei A. Efros,et al. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[10] Pietro Perona,et al. Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Yu-Gang Jiang,et al. Image Block Augmentation for One-Shot Learning , 2019, AAAI.
[12] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[13] Shih-Fu Chang,et al. Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks , 2018, NeurIPS.
[14] Yoshua Bengio,et al. MetaGAN: An Adversarial Approach to Few-Shot Learning , 2018, NeurIPS.
[15] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[16] Hugo Larochelle,et al. Modulating early visual processing by language , 2017, NIPS.
[17] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[18] Amos J. Storkey,et al. Augmenting Image Classifiers Using Data Augmentation Generative Adversarial Networks , 2018, ICANN.
[19] Richard S. Zemel,et al. Prototypical Networks for Few-shot Learning , 2017, NIPS.
[20] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[21] Martial Hebert,et al. Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs , 2016, NIPS.
[22] Yuichi Yoshida,et al. Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.
[23] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Tomas Pfister,et al. Learning from Simulated and Unsupervised Images through Adversarial Training , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Martial Hebert,et al. Low-Shot Learning from Imaginary Data , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[26] Yu-Chiang Frank Wang,et al. A Closer Look at Few-shot Classification , 2019, ICLR.
[27] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[28] Oriol Vinyals,et al. Matching Networks for One Shot Learning , 2016, NIPS.
[29] Bogdan Raducanu,et al. Transferring GANs: generating images from limited data , 2018, ECCV.
[30] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[31] Cordelia Schmid,et al. How good is my GAN? , 2018, ECCV.
[32] 拓海 杉山,et al. “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .
[33] Jonathon Shlens,et al. A Learned Representation For Artistic Style , 2016, ICLR.
[34] Tatsuya Harada,et al. Image Generation From Small Datasets via Batch Statistics Adaptation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[35] Pietro Perona,et al. The Caltech-UCSD Birds-200-2011 Dataset , 2011 .
[36] Rogério Schmidt Feris,et al. Delta-encoder: an effective sample synthesis method for few-shot object recognition , 2018, NeurIPS.
[37] Martial Hebert,et al. Image Deformation Meta-Networks for One-Shot Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Bartunov Sergey,et al. Meta-Learning with Memory-Augmented Neural Networks , 2016 .