Generative Interventions for Causal Learning
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Hao Wang | Carl Vondrick | Chengzhi Mao | Junfeng Yang | Amogh Gupta | Augustine Cha | Carl Vondrick | Junfeng Yang | Chengzhi Mao | Hongya Wang | Amogh Gupta | Augustine Cha
[1] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[2] Claus Aranha,et al. Data Augmentation Using GANs , 2019, ArXiv.
[3] Martial Hebert,et al. Low-Shot Learning from Imaginary Data , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[4] Stefano Soatto,et al. Emergence of Invariance and Disentanglement in Deep Representations , 2017, 2018 Information Theory and Applications Workshop (ITA).
[5] Olga Russakovsky,et al. Fair Attribute Classification through Latent Space De-biasing , 2020, ArXiv.
[6] Graham W. Taylor,et al. Improved Regularization of Convolutional Neural Networks with Cutout , 2017, ArXiv.
[7] Hayit Greenspan,et al. Synthetic data augmentation using GAN for improved liver lesion classification , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[8] Suman V. Ravuri,et al. Classification Accuracy Score for Conditional Generative Models , 2019, NeurIPS.
[9] Jimeng Sun,et al. Causal Regularization , 2019, NeurIPS.
[10] Walter Karlen,et al. CXPlain: Causal Explanations for Model Interpretation under Uncertainty , 2019, NeurIPS.
[11] Jiaying Liu,et al. Demystifying Neural Style Transfer , 2017, IJCAI.
[12] Bernhard Schölkopf,et al. Elements of Causal Inference: Foundations and Learning Algorithms , 2017 .
[13] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[14] J. Pearl. Causality: Models, Reasoning and Inference , 2000 .
[15] Qiang Wang,et al. Adversarial AutoAugment , 2019, ICLR.
[16] Phillip Isola,et al. On the "steerability" of generative adversarial networks , 2019, ICLR.
[17] Honglak Lee,et al. Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.
[18] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[19] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[20] Leon A. Gatys,et al. Image Style Transfer Using Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Silvio Savarese,et al. Causal Induction from Visual Observations for Goal Directed Tasks , 2019, ArXiv.
[22] Pietro Perona,et al. Visual Causal Feature Learning , 2014, UAI.
[23] David Haussler,et al. Exploiting Generative Models in Discriminative Classifiers , 1998, NIPS.
[24] Bernhard Scholkopf. Causality for Machine Learning , 2019 .
[25] Benjamin Recht,et al. Do ImageNet Classifiers Generalize to ImageNet? , 2019, ICML.
[26] Dina Katabi,et al. Continuously Indexed Domain Adaptation , 2020, ICML.
[27] Ali Razavi,et al. Generating Diverse High-Fidelity Images with VQ-VAE-2 , 2019, NeurIPS.
[28] Bolei Zhou,et al. Seeing What a GAN Cannot Generate , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[29] Huan Liu,et al. Deep causal representation learning for unsupervised domain adaptation , 2019, ArXiv.
[30] Zengchang Qin,et al. Emotion Classification with Data Augmentation Using Generative Adversarial Networks , 2018, PAKDD.
[31] Jaakko Lehtinen,et al. GANSpace: Discovering Interpretable GAN Controls , 2020, NeurIPS.
[32] Minyoung Huh,et al. Transforming and Projecting Images into Class-conditional Generative Networks , 2020, ECCV.
[33] Thomas G. Dietterich,et al. Benchmarking Neural Network Robustness to Common Corruptions and Perturbations , 2018, ICLR.
[34] Quoc V. Le,et al. AutoAugment: Learning Augmentation Policies from Data , 2018, ArXiv.
[35] Matthias Bethge,et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness , 2018, ICLR.
[36] Quoc V. Le,et al. Adversarial Examples Improve Image Recognition , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Taghi M. Khoshgoftaar,et al. A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.
[38] Jeff Donahue,et al. Large Scale GAN Training for High Fidelity Natural Image Synthesis , 2018, ICLR.
[39] Jakub M. Tomczak,et al. Designing Data Augmentation for Simulating Interventions , 2020, ArXiv.
[40] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[41] Constantinos Daskalakis,et al. Learning and Testing Causal Models with Interventions , 2018, NeurIPS.
[42] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Boris Katz,et al. ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models , 2019, NeurIPS.
[44] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[45] Yue Wang,et al. Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need? , 2020, ECCV.
[46] David Lopez-Paz,et al. Invariant Risk Minimization , 2019, ArXiv.
[47] Vladimir Vapnik,et al. Principles of Risk Minimization for Learning Theory , 1991, NIPS.
[48] Carlos D. Castillo,et al. Generate to Adapt: Aligning Domains Using Generative Adversarial Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[49] Jonas Peters,et al. Causal inference by using invariant prediction: identification and confidence intervals , 2015, 1501.01332.
[50] Amos J. Storkey,et al. Data Augmentation Generative Adversarial Networks , 2017, ICLR 2018.
[51] Taesung Park,et al. CyCADA: Cycle-Consistent Adversarial Domain Adaptation , 2017, ICML.
[52] Alexei A. Efros,et al. Generative Visual Manipulation on the Natural Image Manifold , 2016, ECCV.
[53] Luis Perez,et al. The Effectiveness of Data Augmentation in Image Classification using Deep Learning , 2017, ArXiv.