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[1] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[2] Yang Song,et al. Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[3] Martin Magill,et al. Neural Networks Trained to Solve Differential Equations Learn General Representations , 2018, NeurIPS.
[4] Leonidas J. Guibas,et al. Taskonomy: Disentangling Task Transfer Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[5] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Quoc V. Le,et al. Domain Adaptive Transfer Learning with Specialist Models , 2018, ArXiv.
[7] Koby Crammer,et al. Learning Bounds for Domain Adaptation , 2007, NIPS.
[8] Aleksey Boyko,et al. Detecting Cancer Metastases on Gigapixel Pathology Images , 2017, ArXiv.
[9] H. Ahsan. Diabetic retinopathy--biomolecules and multiple pathophysiology. , 2015, Diabetes & metabolic syndrome.
[10] Dayong Wang,et al. Deep Learning for Identifying Metastatic Breast Cancer , 2016, ArXiv.
[11] Andrea Vedaldi,et al. Deep Image Prior , 2017, International Journal of Computer Vision.
[12] Lior Wolf,et al. A theoretical framework for deep transfer learning , 2016 .
[13] Jon M. Kleinberg,et al. Direct Uncertainty Prediction for Medical Second Opinions , 2018, ICML.
[14] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[15] Andrew Y. Ng,et al. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning , 2017, ArXiv.
[16] Ronald M. Summers,et al. Interleaved text/image Deep Mining on a large-scale radiology database , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Andrea Vedaldi,et al. Understanding deep image representations by inverting them , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[19] Kaiming He,et al. Rethinking ImageNet Pre-Training , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[20] Jascha Sohl-Dickstein,et al. SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability , 2017, NIPS.
[21] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[22] Samy Bengio,et al. Insights on representational similarity in neural networks with canonical correlation , 2018, NeurIPS.
[23] Adam Lopez,et al. Understanding Learning Dynamics Of Language Models with SVCCA , 2018, NAACL.
[24] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Yizhou Yu,et al. Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-Tuning , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Subhashini Venugopalan,et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.
[27] Matthias Bethge,et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness , 2018, ICLR.
[28] Ronald M. Summers,et al. ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.