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Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification

Fabio Perez, Sandra Avila, Eduardo Valle
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Whenever possible, the best approach for melanoma classification is still to create ensembles of multiple models.  ...  For small ensembles, we found a slight advantage on the second approach but found that random choice was also competitive.  ...  Avila is partially funded by Google LARA 2018. E. Valle is partially funded by a CNPq PQ-2 grant (311905/2017-0).  ... 
doi:10.1109/cvprw.2019.00336 dblp:conf/cvpr/PerezAV19 fatcat:bphj5vjdxzbftjrni3gw7izw3m

Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification [article]

Fábio Perez and Sandra Avila and Eduardo Valle
2019 arXiv   pre-print
Whenever possible, the best approach for melanoma classification is still to create ensembles of multiple models.  ...  For small ensembles, we found a slight advantage on the second approach but found that random choice was also competitive.  ...  Avila is partially funded by Google LARA 2018. E. Valle is partially funded by a CNPq PQ-2 grant (311905/2017-0).  ... 
arXiv:1904.12724v1 fatcat:il2m2o7ub5c7hnlehhl7oqga7y

An Empirical Evaluation of Current Convolutional Architectures' Ability to Manage Nuisance Location and Scale Variability [article]

Nikolaos Karianakis, Jingming Dong, Stefano Soatto
2016 arXiv   pre-print
very effective at marginalizing nuisance variability.  ...  We also quantify the effects of context on the overall classification task and its impact on the performance of CNNs, and propose improved sampling techniques for heuristic proposal schemes that improve  ...  We gratefully acknowledge NVIDIA Corporation for donating a K40 GPU that was used in support of some of the experiments.  ... 
arXiv:1505.06795v2 fatcat:wisopx623fdgbhmhz5jaufv2ha

An Empirical Evaluation of Current Convolutional Architectures' Ability to Manage Nuisance Location and Scale Variability

Nikolaos Karianakis, Jingming Dong, Stefano Soatto
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
very effective at marginalizing nuisance variability.  ...  We also quantify the effects of context on the overall classification task and its impact on the performance of CNNs, and propose improved sampling techniques for heuristic proposal schemes that improve  ...  We gratefully acknowledge NVIDIA Corporation for donating a K40 GPU that was used in support of some of the experiments.  ... 
doi:10.1109/cvpr.2016.481 dblp:conf/cvpr/KarianakisDS16 fatcat:i3sdwsrsb5gxxgo77xcnviioye

End-to-End Training of Hybrid CNN-CRF Models for Stereo [article]

Patrick Knöbelreiter, Christian Reinbacher, Alexander Shekhovtsov, Thomas Pock
2017 arXiv   pre-print
We propose a novel and principled hybrid CNN+CRF model for stereo estimation.  ...  For inference, we apply a recently proposed highly parallel dual block descent algorithm which only needs a small fixed number of iterations to compute a high-quality approximate minimizer.  ...  This requires robustness to all kinds of visual nuisances as well as a good prior model of the 3D environment.  ... 
arXiv:1611.10229v2 fatcat:kmqnpznsxjdvxknu5gccc4wz24

What Makes for Good Views for Contrastive Learning? [article]

Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, Phillip Isola
2020 arXiv   pre-print
As a by-product, we achieve a new state-of-the-art accuracy on unsupervised pre-training for ImageNet classification (73% top-1 linear readout with a ResNet-50).  ...  Despite its success, the influence of different view choices has been less studied.  ...  Yonglong is grateful to Zhoutong Zhang for encouragement and feedback on experimental design.  ... 
arXiv:2005.10243v3 fatcat:36l33k5k7vhp3gylsyrigifyyq

An adversarial approach for the robust classification of pneumonia from chest radiographs

Joseph D. Janizek, Gabriel Erion, Alex J. DeGrave, Su-In Lee
2020 Proceedings of the ACM Conference on Health, Inference, and Learning  
Specically, we demonstrate improved out-of-hospital generalization performance of a pneumonia classier by training a model that is invariant to the view position of chest radiographs (anterior-posterior  ...  In order for these models to be safely deployed, we would like to ensure that they do not use confounding variables to make their classication, and that they will work well even when tested on images from  ...  Su-In Lee's lab for their valuable general feedback on the project.  ... 
doi:10.1145/3368555.3384458 dblp:conf/chil/JanizekEDL20 fatcat:bovemdso7jddndlceujn6znuk4

AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference

Andac Demir, Toshiaki Koike-Akino, Ye Wang, Deniz Erdogmus
2021 IEEE Access  
We benchmark the framework on several public datasets, and provide analysis of its capability for subject-transfer learning with/without variational modeling and adversarial training.  ...  Auto-Bayes also enables learning disentangled representations, where the latent variable is split into multiple pieces to impose various relationships with the nuisance variation and task labels.  ...  Although A-CVAE in Fig. 1 (b) may offer nuisance-robust performance through adversarial disentanglement of S from latent Z , there is no guarantee that such a model can perform well across different datasets  ... 
doi:10.1109/access.2021.3064530 fatcat:hfwenaojunegfbytlcvc73z2e4

AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference [article]

Andac Demir, Toshiaki Koike-Akino, Ye Wang, Deniz Erdogmus
2020 arXiv   pre-print
We benchmark the framework on several public datasets, where we have access to subject and class labels during training, and provide analysis of its capability for subject-transfer learning with/without  ...  Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning.  ...  Although A-CVAE in Fig. 1 (b) may offer nuisance-robust performance through adversarial disentanglement of S from latent Z, there is no guarantee that such a model can perform well across different datasets  ... 
arXiv:2007.01255v2 fatcat:su2j7qcsfndsnbxp4nb2dnv2c4

An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs [article]

Joseph D. Janizek, Gabriel Erion, Alex J. DeGrave, Su-In Lee
2020 arXiv   pre-print
Specifically, we demonstrate improved out-of-hospital generalization performance of a pneumonia classifier by training a model that is invariant to the view position of chest radiographs (anterior-posterior  ...  In order for these models to be safely deployed, we would like to ensure that they do not use confounding variables to make their classification, and that they will work well even when tested on images  ...  Su-In Lee's lab for their valuable general feedback on the project.  ... 
arXiv:2001.04051v1 fatcat:sgpos7poz5ewdostefqxhdubbi

End-to-End Training of Hybrid CNN-CRF Models for Stereo

Patrick Knobelreiter, Christian Reinbacher, Alexander Shekhovtsov, Thomas Pock
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
We propose a novel method for stereo estimation, combining advantages of convolutional neural networks (CNNs) and optimization-based approaches.  ...  The trained hybrid model with shallow CNNs is comparable to state-of-the-art deep models in both time and performance.  ...  This requires robustness to all kinds of visual nuisances as well as a good prior model of the 3D environment.  ... 
doi:10.1109/cvpr.2017.159 dblp:conf/cvpr/KnobelreiterRSP17 fatcat:pve22663nvbsplidi2nwsrdbda

Field Convolutions for Surface CNNs [article]

Thomas W. Mitchel, Vladimir G. Kim, Michael Kazhdan
2021 arXiv   pre-print
The result is a rich notion of convolution which we call field convolution, well-suited for CNNs on surfaces.  ...  We present a novel surface convolution operator acting on vector fields that is based on a simple observation: instead of combining neighboring features with respect to a single coordinate parameterization  ...  The result is a rich notion of convolution which we call field convolution (FC), well-suited for CNNs on surfaces.  ... 
arXiv:2104.03916v2 fatcat:ltt2urfjfndklj2jvvqzymvw2y

Representation Based Complexity Measures for Predicting Generalization in Deep Learning [article]

Parth Natekar, Manik Sharma
2020 arXiv   pre-print
An implementation of our solution is available at https://github.com/parthnatekar/pgdl.  ...  Deep Neural Networks can generalize despite being significantly overparametrized.  ...  Acknowledgements We'd like to thank the organizers of the NeurIPS 2020 Competition on Predicting Generalization in Deep Learning for hosting this competition and for providing a platform for us to test  ... 
arXiv:2012.02775v1 fatcat:6kniioe4rfhmvj3t7qh4yrvkpa

Deep Imbalanced Learning for Face Recognition and Attribute Prediction [article]

Chen Huang, Yining Li, Chen Change Loy, Xiaoou Tang
2019 arXiv   pre-print
We show that it is easy to deploy angular margins between the cluster distributions on a hypersphere manifold.  ...  We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain inter-cluster margins both within and between classes.  ...  These margins are designed angular, which translates well to the inner product based similarity metric on a unit hypersphere.  ... 
arXiv:1806.00194v2 fatcat:3qbovwkh7bfknaudlcjclui4gi

Learning Deep Representation for Imbalanced Classification

Chen Huang, Yining Li, Chen Change Loy, Xiaoou Tang
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain both intercluster and inter-class margins.  ...  We show that the margins can be easily deployed in standard deep learning framework through quintuplet instance sampling and the associated triple-header hinge loss.  ...  This work is partially supported by SenseTime Group Limited and the Hong Kong Innovation and Technology Support Programme.  ... 
doi:10.1109/cvpr.2016.580 dblp:conf/cvpr/HuangLLT16 fatcat:pr23evghqzcoboup7gbfrq63uq
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