Towards Scalable Image Classifier Learning with Noisy Labels via Domain Adaptation

In this chapter we focus on learning image classifiers with noisy labels through domain adaptation. Existing approaches for learning image classifiers with noisy labels using human supervision are generally difficult to scale to large set of classes as manual labeling images for all classes are expensive and time-consuming. Approaches that address noisy labels without manual labeling efforts are scalable but less effective in lack of reliable supervision. Transfer learning reconciles this conflict through transferring knowledge from classes with exemplary human supervision (source domains) to classes where data are not manually verified (target domains), relaxing the requirement of human efforts. In this chapter, we introduce a transfer learning set-up for tackling noisy labels, and review CleanNet, the first neural network model that practically implements this set-up, and explore future directions of this topic.

[1]  Jonathan Krause,et al.  The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition , 2015, ECCV.

[2]  Gang Hua,et al.  Unsupervised One-Class Learning for Automatic Outlier Removal , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Wei Li,et al.  WebVision Database: Visual Learning and Understanding from Web Data , 2017, ArXiv.

[4]  Marc'Aurelio Ranzato,et al.  DeViSE: A Deep Visual-Semantic Embedding Model , 2013, NIPS.

[5]  Martial Hebert,et al.  Semi-Supervised Self-Training of Object Detection Models , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[6]  David Yarowsky,et al.  Unsupervised Word Sense Disambiguation Rivaling Supervised Methods , 1995, ACL.

[7]  Jordi Pont-Tuset,et al.  The Open Images Dataset V4 , 2018, International Journal of Computer Vision.

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

[9]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[10]  David A. Shamma,et al.  YFCC100M , 2015, Commun. ACM.

[11]  Albert Fornells,et al.  A study of the effect of different types of noise on the precision of supervised learning techniques , 2010, Artificial Intelligence Review.

[12]  Subhransu Maji,et al.  Fine-Grained Visual Classification of Aircraft , 2013, ArXiv.

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

[14]  Jonathon Shlens,et al.  Fast, Accurate Detection of 100,000 Object Classes on a Single Machine , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Tatsuya Harada,et al.  Asymmetric Tri-training for Unsupervised Domain Adaptation , 2017, ICML.

[16]  Richard Nock,et al.  Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  M. Verleysen,et al.  Classification in the Presence of Label Noise: A Survey , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[19]  Ruslan Salakhutdinov,et al.  Learning Robust Visual-Semantic Embeddings , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[20]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[21]  Kiyoharu Aizawa,et al.  Joint Optimization Framework for Learning with Noisy Labels , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[22]  Yao Li,et al.  Attend in Groups: A Weakly-Supervised Deep Learning Framework for Learning from Web Data , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Jason Weston,et al.  WSABIE: Scaling Up to Large Vocabulary Image Annotation , 2011, IJCAI.

[24]  Le Lu,et al.  DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning , 2018, Journal of medical imaging.

[25]  Nir Shavit,et al.  Deep Learning is Robust to Massive Label Noise , 2017, ArXiv.

[26]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[27]  Ari Rappoport,et al.  Self-Training for Enhancement and Domain Adaptation of Statistical Parsers Trained on Small Datasets , 2007, ACL.

[28]  Pietro Perona,et al.  Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Bolei Zhou,et al.  Places: A 10 Million Image Database for Scene Recognition , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Abhinav Gupta,et al.  Learning from Noisy Large-Scale Datasets with Minimal Supervision , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Trevor Darrell,et al.  Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Tatsuya Harada,et al.  Maximum Classifier Discrepancy for Unsupervised Domain Adaptation , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[33]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[34]  Lei Zhang,et al.  CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[35]  Eugene Charniak,et al.  Effective Self-Training for Parsing , 2006, NAACL.

[36]  Jun Suzuki,et al.  Semi-Supervised Sequential Labeling and Segmentation Using Giga-Word Scale Unlabeled Data , 2008, ACL.

[37]  Yanchun Zhang,et al.  Support Vector Machine for Outlier Detection in Breast Cancer Survivability Prediction , 2008, APWeb Workshops.

[38]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[39]  Xinlei Chen,et al.  Webly Supervised Learning of Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[40]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[41]  Mary P. Harper,et al.  Self-Training PCFG Grammars with Latent Annotations Across Languages , 2009, EMNLP.

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

[43]  Walter Daelemans,et al.  Predicting the Effectiveness of Self-Training: Application to Sentiment Classification , 2016, ArXiv.

[44]  Matthieu Guillaumin,et al.  Food-101 - Mining Discriminative Components with Random Forests , 2014, ECCV.

[45]  Yale Song,et al.  Learning from Noisy Labels with Distillation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[46]  Trevor Darrell,et al.  Auxiliary Image Regularization for Deep CNNs with Noisy Labels , 2015, ICLR.

[47]  Yinda Zhang,et al.  LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop , 2015, ArXiv.

[48]  Gang Hua,et al.  Learning Discriminative Reconstructions for Unsupervised Outlier Removal , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).