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[1] Gordon Wetzstein,et al. DeepVoxels: Learning Persistent 3D Feature Embeddings , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Vineeth N. Balasubramanian,et al. Zero-Shot Task Transfer , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Han Zhang,et al. Self-Attention Generative Adversarial Networks , 2018, ICML.
[4] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[5] Paolo Favaro,et al. Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles , 2016, ECCV.
[6] Thomas Brox,et al. Discriminative Unsupervised Feature Learning with Convolutional Neural Networks , 2014, NIPS.
[7] Hui Li,et al. Semisupervised Multitask Learning , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] Silvio Savarese,et al. 3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[9] George Papandreou,et al. Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[10] Martial Hebert,et al. Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis , 2020, ArXiv.
[11] Hayit Greenspan,et al. GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification , 2018, Neurocomputing.
[12] Memory, Imagination, and Learning: Connected by the Story. , 1989 .
[13] Joel Pearson,et al. The human imagination: the cognitive neuroscience of visual mental imagery , 2019, Nature Reviews Neuroscience.
[14] Kieran Egan,et al. Imagination in Teaching and Learning: The Middle School Years , 1992 .
[15] Concetto Spampinato,et al. Semi Supervised Semantic Segmentation Using Generative Adversarial Network , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[16] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[18] Qiang Yang,et al. An Overview of Multi-task Learning , 2018 .
[19] Jitendra Malik,et al. Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[20] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[21] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[22] Sebastian Ruder,et al. An Overview of Multi-Task Learning in Deep Neural Networks , 2017, ArXiv.
[23] Leonidas Guibas,et al. Robust Learning Through Cross-Task Consistency , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Ali Borji,et al. Pros and Cons of GAN Evaluation Measures , 2018, Comput. Vis. Image Underst..
[25] Ruslan Salakhutdinov,et al. On the Quantitative Analysis of Decoder-Based Generative Models , 2016, ICLR.
[26] Michael Cole,et al. “Minding the Gap”: Imagination, Creativity and Human Cognition , 2011, Integrative psychological & behavioral science.
[27] Yong-Liang Yang,et al. RenderNet: A deep convolutional network for differentiable rendering from 3D shapes , 2018, NeurIPS.
[28] Jitendra Malik,et al. Which Tasks Should Be Learned Together in Multi-task Learning? , 2019, ICML.
[29] Yong Jae Lee,et al. Cross-Domain Self-Supervised Multi-task Feature Learning Using Synthetic Imagery , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[30] Iasonas Kokkinos,et al. UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Wonhee Lee,et al. Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Shijian Lu,et al. Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes , 2018, ECCV.
[33] Zengchang Qin,et al. Emotion Classification with Data Augmentation Using Generative Adversarial Networks , 2018, PAKDD.
[34] Jan Kautz,et al. Self-Supervised Viewpoint Learning From Image Collections , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Stefano Mattoccia,et al. Generative Adversarial Networks for Unsupervised Monocular Depth Prediction , 2018, ECCV Workshops.
[36] Suchendra M. Bhandarkar,et al. Monocular Depth Prediction Using Generative Adversarial Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[37] Camille Couprie,et al. Semantic Segmentation using Adversarial Networks , 2016, NIPS 2016.
[38] Martial Hebert,et al. Low-Shot Learning from Imaginary Data , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[39] Leonidas J. Guibas,et al. Taskonomy: Disentangling Task Transfer Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[40] Changick Kim,et al. Self-Ensembling With GAN-Based Data Augmentation for Domain Adaptation in Semantic Segmentation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[41] Derek Hoiem,et al. Indoor Segmentation and Support Inference from RGBD Images , 2012, ECCV.
[42] Rameswar Panda,et al. AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning , 2020, NeurIPS.
[43] Rob Fergus,et al. Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[44] Cordelia Schmid,et al. How good is my GAN? , 2018, ECCV.
[45] Alexei A. Efros,et al. Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Taghi M. Khoshgoftaar,et al. A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.
[47] Jeff Donahue,et al. Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.
[48] Hugo Larochelle,et al. Modulating early visual processing by language , 2017, NIPS.
[49] Paolo Favaro,et al. Representation Learning by Learning to Count , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[50] Martial Hebert,et al. Cross-Stitch Networks for Multi-task Learning , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[51] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[52] Ke Yan,et al. Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks , 2019, Scientific Reports.
[53] Nicu Sebe,et al. Unsupervised Adversarial Depth Estimation Using Cycled Generative Networks , 2018, 2018 International Conference on 3D Vision (3DV).
[54] Zhao Chen,et al. GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks , 2017, ICML.
[55] Christoph H. Lampert,et al. Multi-task Learning with Labeled and Unlabeled Tasks , 2016, ICML.