Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts
暂无分享,去创建一个
[1] S. Levine,et al. MEMO: Test Time Robustness via Adaptation and Augmentation , 2021, NeurIPS.
[2] Sotirios A. Tsaftaris,et al. Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training , 2021, DART/FAIR@MICCAI.
[3] Aaron Carass,et al. Autoencoder based self-supervised test-time adaptation for medical image analysis , 2021, Medical Image Anal..
[4] Sergio Escalera,et al. Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge , 2021, IEEE Transactions on Medical Imaging.
[5] Felix Wiewel,et al. MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption , 2021, AISTATS.
[6] Sotirios A. Tsaftaris,et al. Learning to Segment From Scribbles Using Multi-Scale Adversarial Attention Gates , 2021, IEEE Transactions on Medical Imaging.
[7] Chen Change Loy,et al. Domain Generalization: A Survey , 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] Hao Guan,et al. Domain Adaptation for Medical Image Analysis: A Survey , 2021, IEEE Transactions on Biomedical Engineering.
[9] Hongxia Jin,et al. Negative Data Augmentation , 2021, ICLR.
[10] Hyunwoo J. Kim,et al. Online Continual Learning in Image Classification: An Empirical Survey , 2021, Neurocomputing.
[11] Paul Honeine,et al. High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey , 2020, Comput. Vis. Image Underst..
[12] Enzo Ferrante,et al. Post-DAE: Anatomically Plausible Segmentation via Post-Processing With Denoising Autoencoders , 2020, IEEE Transactions on Medical Imaging.
[13] E. Konukoglu,et al. Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation , 2020, Medical Image Anal..
[14] Kenji Fukumizu,et al. Smoothness and Stability in GANs , 2020, ICLR.
[15] Andreas Nürnberger,et al. CHAOS Challenge - Combined (CT-MR) Healthy Abdominal Organ Segmentation , 2020, Medical Image Anal..
[16] Daniel C. Castro,et al. Causality matters in medical imaging , 2019, Nature Communications.
[17] Yu-Hsing Wang,et al. Is Discriminator a Good Feature Extractor? , 2019, ArXiv.
[18] Daniel C. Castro,et al. Domain Generalization via Model-Agnostic Learning of Semantic Features , 2019, NeurIPS.
[19] Ilkay Öksüz,et al. A Topological Loss Function for Deep-Learning Based Image Segmentation Using Persistent Homology , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] Alexei A. Efros,et al. Test-Time Training with Self-Supervision for Generalization under Distribution Shifts , 2019, ICML.
[21] Tinne Tuytelaars,et al. A Continual Learning Survey: Defying Forgetting in Classification Tasks , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[22] Xiaowei Ding,et al. Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation , 2019, Medical Image Anal..
[23] Olivier Bernard,et al. Cardiac MRI Segmentation with Strong Anatomical Guarantees , 2019, MICCAI.
[24] Geoffrey E. Hinton,et al. When Does Label Smoothing Help? , 2019, NeurIPS.
[25] Yuki M. Asano,et al. A critical analysis of self-supervision, or what we can learn from a single image , 2019, ICLR.
[26] Mert R. Sabuncu,et al. Unsupervised deep learning for Bayesian brain MRI segmentation , 2019, MICCAI.
[27] Hwee Kuan Lee,et al. Fence GAN: Towards Better Anomaly Detection , 2019, 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI).
[28] Sotirios A. Tsaftaris,et al. Disentangled representation learning in cardiac image analysis , 2019, Medical Image Anal..
[29] Alexandre Alahi,et al. Collaborative Sampling in Generative Adversarial Networks , 2019, AAAI.
[30] Christopher Joseph Pal,et al. A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms , 2019, ICLR.
[31] Chuan Sheng Foo,et al. Adversarially Learned Anomaly Detection , 2018, 2018 IEEE International Conference on Data Mining (ICDM).
[32] P. Babyn,et al. Generative Adversarial Network in Medical Imaging: A Review , 2018, Medical Image Anal..
[33] Xiaohua Zhai,et al. A Large-Scale Study on Regularization and Normalization in GANs , 2018, ICML.
[34] Benjamin Recht,et al. Do CIFAR-10 Classifiers Generalize to CIFAR-10? , 2018, ArXiv.
[35] Alex ChiChung Kot,et al. Domain Generalization with Adversarial Feature Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[36] Mert R. Sabuncu,et al. Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[37] Xin Yang,et al. Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? , 2018, IEEE Transactions on Medical Imaging.
[38] Eric Granger,et al. Constrained‐CNN losses for weakly supervised segmentation☆ , 2018, Medical Image Anal..
[39] Josien P. W. Pluim,et al. Not‐so‐supervised: A survey of semi‐supervised, multi‐instance, and transfer learning in medical image analysis , 2018, Medical Image Anal..
[40] Minjung Kim,et al. Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks , 2018, ICLR.
[41] Yuichi Yoshida,et al. Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.
[42] Zhen Wang,et al. On the Effectiveness of Least Squares Generative Adversarial Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[43] Richard E. Turner,et al. Variational Continual Learning , 2017, ICLR.
[44] Sotirios A. Tsaftaris,et al. Robust Multi-modal MR Image Synthesis , 2017, MICCAI.
[45] Kilian Q. Weinberger,et al. On Calibration of Modern Neural Networks , 2017, ICML.
[46] Konstantinos Kamnitsas,et al. Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation , 2017, IEEE Transactions on Medical Imaging.
[47] Jiwon Kim,et al. Continual Learning with Deep Generative Replay , 2017, NIPS.
[48] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[49] Tomas Pfister,et al. Learning from Simulated and Unsupervised Images through Adversarial Training , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Andrei A. Rusu,et al. Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.
[51] Lucas Theis,et al. Amortised MAP Inference for Image Super-resolution , 2016, ICLR.
[52] Jorge Nocedal,et al. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima , 2016, ICLR.
[53] Ghassan Hamarneh,et al. Incorporating prior knowledge in medical image segmentation: a survey , 2016, ArXiv.
[54] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[55] Trevor Darrell,et al. Adversarial Feature Learning , 2016, ICLR.
[56] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[57] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[58] Vibhav Vineet,et al. Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[59] Christian Szegedy,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[60] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[61] Kevin Barraclough,et al. I and i , 2001, BMJ : British Medical Journal.
[62] Anthony V. Robins,et al. Catastrophic Forgetting, Rehearsal and Pseudorehearsal , 1995, Connect. Sci..
[63] R. Stephenson. A and V , 1962, The British journal of ophthalmology.
[64] Fahed Abdallah,et al. A Surprisingly Effective Perimeter-based Loss for Medical Image Segmentation , 2021, MIDL.
[65] Trevor Darrell,et al. Tent: Fully Test-Time Adaptation by Entropy Minimization , 2021, ICLR.
[66] Vaishnavh Nagarajan. Theoretical Insights into Memorization in GANs , 2019 .
[67] W. Hager,et al. and s , 2019, Shallow Water Hydraulics.
[68] Tej Singh,et al. Completeness , 2019, Introduction to Topology.
[69] Jinsung Yoon,et al. GENERATIVE ADVERSARIAL NETS , 2018 .
[70] Nicholas Ayache,et al. A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images , 2014, Medical Image Anal..
[71] Bernhard Schölkopf,et al. Semi-supervised Learning in Causal and Anticausal Settings , 2013, Empirical Inference.
[72] W. Marsden. I and J , 2012 .
[73] L. Sehgal,et al. Γ and B , 2004 .
[74] I. Miyazaki,et al. AND T , 2022 .
[75] and as an in , 2022 .