Concept whitening for interpretable image recognition
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[1] Avrim Blum,et al. Foundations of Data Science , 2020 .
[2] Sercan Ö. Arik,et al. On Completeness-aware Concept-Based Explanations in Deep Neural Networks , 2019, NeurIPS.
[3] Tianfu Wu,et al. Towards Interpretable Object Detection by Unfolding Latent Structures , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[4] Chun-Liang Li,et al. On Concept-Based Explanations in Deep Neural Networks , 2019, ArXiv.
[5] Quoc V. Le,et al. Saccader: Improving Accuracy of Hard Attention Models for Vision , 2019, NeurIPS.
[6] Ludovic Denoyer,et al. EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction , 2019, ArXiv.
[7] Ole-Christoffer Granmo,et al. The Convolutional Tsetlin Machine , 2019, ArXiv.
[8] Lei Huang,et al. Iterative Normalization: Beyond Standardization Towards Efficient Whitening , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[9] James Zou,et al. Towards Automatic Concept-based Explanations , 2019, NeurIPS.
[10] Mario Lezcano Casado,et al. Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group , 2019, ICML.
[11] Cynthia Rudin,et al. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.
[12] Cynthia Rudin,et al. This Looks Like That: Deep Learning for Interpretable Image Recognition , 2018 .
[13] Nicu Sebe,et al. Whitening and Coloring Batch Transform for GANs , 2018, ICLR.
[14] Aaron J. Fisher,et al. All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously , 2018, J. Mach. Learn. Res..
[15] Tianfu Wu,et al. AOGNets: Compositional Grammatical Architectures for Deep Learning , 2017, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Bolei Zhou,et al. Interpreting Deep Visual Representations via Network Dissection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[17] Thomas Villmann,et al. Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components , 2019, NeurIPS.
[18] Been Kim,et al. Sanity Checks for Saliency Maps , 2018, NeurIPS.
[19] Bolei Zhou,et al. Interpretable Basis Decomposition for Visual Explanation , 2018, ECCV.
[20] Zoubin Ghahramani,et al. Discovering Interpretable Representations for Both Deep Generative and Discriminative Models , 2018, ICML.
[21] Bolei Zhou,et al. Places: A 10 Million Image Database for Scene Recognition , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[22] Quanshi Zhang,et al. Unsupervised Learning of Neural Networks to Explain Neural Networks , 2018, ArXiv.
[23] Lei Huang,et al. Decorrelated Batch Normalization , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[24] Martin Wattenberg,et al. TCAV: Relative concept importance testing with Linear Concept Activation Vectors , 2018 .
[25] Guillermo Sapiro,et al. OLE: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[26] Martin Wattenberg,et al. Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) , 2017, ICML.
[27] Cynthia Rudin,et al. Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions , 2017, AAAI.
[28] Quanshi Zhang,et al. Interpretable Convolutional Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[29] Xianglong Liu,et al. Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks , 2017, AAAI.
[30] Ping Luo,et al. Learning Deep Architectures via Generalized Whitened Neural Networks , 2017, ICML.
[31] Martin Wattenberg,et al. SmoothGrad: removing noise by adding noise , 2017, ArXiv.
[32] Christopher Joseph Pal,et al. On orthogonality and learning recurrent networks with long term dependencies , 2017, ICML.
[33] James Bailey,et al. Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections , 2016, ICML.
[34] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[35] Abhishek Das,et al. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[36] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Basura Fernando,et al. Generalized BackPropagation, Étude De Cas: Orthogonality , 2016, ArXiv.
[38] Les E. Atlas,et al. Full-Capacity Unitary Recurrent Neural Networks , 2016, NIPS.
[39] Pieter Abbeel,et al. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.
[40] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[41] Ross B. Girshick,et al. Reducing Overfitting in Deep Networks by Decorrelating Representations , 2015, ICLR.
[42] Razvan Pascanu,et al. Natural Neural Networks , 2015, NIPS.
[43] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[44] Koray Kavukcuoglu,et al. Multiple Object Recognition with Visual Attention , 2014, ICLR.
[45] Pierre Sermanet,et al. Attention for Fine-Grained Categorization , 2014, ICLR.
[46] Bolei Zhou,et al. Object Detectors Emerge in Deep Scene CNNs , 2014, ICLR.
[47] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[48] Alex Graves,et al. Recurrent Models of Visual Attention , 2014, NIPS.
[49] Pietro Perona,et al. Microsoft COCO: Common Objects in Context , 2014, ECCV.
[50] Andrew Zisserman,et al. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.
[51] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[52] Wotao Yin,et al. A feasible method for optimization with orthogonality constraints , 2013, Math. Program..
[53] James Hays,et al. SUN attribute database: Discovering, annotating, and recognizing scene attributes , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[54] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[55] L. C. Rose. Recognizing neoplastic skin lesions: a photo guide. , 1998, American family physician.