Auxiliary Image Regularization for Deep CNNs with Noisy Labels

Precisely-labeled data sets with sufficient amount of samples are very important for training deep convolutional neural networks (CNNs). However, many of the available real-world data sets contain erroneously labeled samples and those errors substantially hinder the learning of very accurate CNN models. In this work, we consider the problem of training a deep CNN model for image classification with mislabeled training samples - an issue that is common in real image data sets with tags supplied by amateur users. To solve this problem, we propose an auxiliary image regularization technique, optimized by the stochastic Alternating Direction Method of Multipliers (ADMM) algorithm, that automatically exploits the mutual context information among training images and encourages the model to select reliable images to robustify the learning process. Comprehensive experiments on benchmark data sets clearly demonstrate our proposed regularized CNN model is resistant to label noise in training data.

[1]  Jason Yosinski,et al.  Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[3]  Fei-Fei Li,et al.  What Does Classifying More Than 10, 000 Image Categories Tell Us? , 2010, ECCV.

[4]  R. Tibshirani,et al.  A note on the group lasso and a sparse group lasso , 2010, 1001.0736.

[5]  Rob Fergus,et al.  Learning from Noisy Labels with Deep Neural Networks , 2014, ICLR.

[6]  P. Lions,et al.  Splitting Algorithms for the Sum of Two Nonlinear Operators , 1979 .

[7]  Ali Farhadi,et al.  Image Classification and Retrieval from User-Supplied Tags , 2014, ArXiv.

[8]  Joan Bruna,et al.  Training Convolutional Networks with Noisy Labels , 2014, ICLR 2014.

[9]  Alberto Del Bimbo,et al.  Socializing the Semantic Gap , 2015, ACM Comput. Surv..

[10]  Xinyun Chen Under Review as a Conference Paper at Iclr 2017 Delving into Transferable Adversarial Ex- Amples and Black-box Attacks , 2016 .

[11]  Julien Mairal,et al.  Optimization with Sparsity-Inducing Penalties , 2011, Found. Trends Mach. Learn..

[12]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[13]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[14]  R. Glowinski,et al.  Sur l'approximation, par éléments finis d'ordre un, et la résolution, par pénalisation-dualité d'une classe de problèmes de Dirichlet non linéaires , 1975 .

[15]  Alexander G. Gray,et al.  Stochastic Alternating Direction Method of Multipliers , 2013, ICML.

[16]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[17]  B. Mercier,et al.  A dual algorithm for the solution of nonlinear variational problems via finite element approximation , 1976 .

[18]  Xiaogang Wang,et al.  Learning from massive noisy labeled data for image classification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, CVPR.

[20]  Guillermo Sapiro,et al.  Supervised Dictionary Learning , 2008, NIPS.

[21]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[22]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[23]  Omer Levy,et al.  Published as a conference paper at ICLR 2018 S IMULATING A CTION D YNAMICS WITH N EURAL P ROCESS N ETWORKS , 2018 .

[24]  Julien Mairal,et al.  Convex optimization with sparsity-inducing norms , 2011 .

[25]  Noah A. Smith,et al.  Making the Most of Bag of Words: Sentence Regularization with Alternating Direction Method of Multipliers , 2014, ICML.

[26]  Suvrit Sra,et al.  Towards an optimal stochastic alternating direction method of multipliers , 2014, ICML.

[27]  Shie Mannor,et al.  Robust Logistic Regression and Classification , 2014, NIPS.