Fetching the paper…

Reading the bibliography…

CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images · Around

2018

CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images

Guo, Sheng, Huang, Weilin, Zhang, Haozhi et al.

Original
PDF
Post on X

Understand

We present a simple yet efficient approach capable of training deep neural networks on large-scale weakly-supervised web images, which are crawled raw from the Internet by using text queries, without any human annotation.

We develop a principled learning strategy by leveraging curriculum learning, with the goal of handling a massive amount of noisy labels and data imbalance effectively.
We design a new learning curriculum by measuring the complexity of data using its distribution density in a feature space, and rank the complexity in an unsupervised manner.
This allows for an efficient implementation of curriculum learning on large-scale web images, resulting in a high-performance CNN model, where the negative impact of noisy labels is reduced substantially.

Built on

Larsen, J., Nonboe, L., Hintz-Madsen, M., Hansen, L.K.: Design of robust neural network classifiers. In: ICASSP (1998)
1998
Earlier work this paper cites.
Brodley, C.E., Friedl, M.A.: Identifying mislabeled training data. CoRR, abs/1106.0219 (1999)
Original
1999
Earlier work this paper cites.
Zhu, X.: Semi-supervised learning literature survey. CoRR, abs/1106.0219 (2005)
Original
2005
Earlier work this paper cites.
Everingham, M., Gool, L.V., Williams, C., Winn, J., Zisserman, A.: The pascal visual object classes challenge 2007 (voc 2007) results (2007). In: URL http://www. pascal-network. org/challenges/VOC/voc2007/workshop/index. html (2008)
2008
Earlier work this paper cites.
Bengio, Y., Louradour, J., Collobert, R., Weston, J.: Curriculum learning. In: ICML. pp. 41–48. ACM (2009)
2009
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: ImageNet: A large-scale hierarchical image database. In: CVPR. pp. 248–255 (2009)
2009
Earlier work this paper cites.
R. Fergus, Y.W., Torralba, A.: Semi-supervised learning in gigantic image collections. In: NIPS (2009)
2009
Earlier work this paper cites.
X. Chen, A.S., Neil, A.G.: Extracting visual knowledge from web data. In: ICCV (2013)
2013
Earlier work this paper cites.
Bossard, L., Guillaumin, M., Van Gool, L.: Food-101–mining discriminative components with random forests. In: ECCV. pp. 446–461. Springer (2014)
2014
Earlier work this paper cites.
Frénay, B., Verleysen, M.: Classification in the presence of label noise: a survey. IEEE transactions on neural networks and learning systems 25
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: ECCV. pp. 740–755 (2014)
2014
Earlier work this paper cites.
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344
2014
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556
Original
2014
Earlier work this paper cites.
Sukhbaatar, S., Fergus, R.: Learning from noisy labels with deep neural networks. CoRR abs/1406.2080
Original
2014
Earlier work this paper cites.

Similar

Hong, S., Noh, H., Han, B.: Decoupled deep neural network for semi-supervised semantic segmentation. In: NIPS. pp. 1495–1503 (2015)
2015
Cited alongside, same era.
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. CoRR abs/1502.03167
Original
2015
Cited alongside, same era.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR. pp. 3431–3440 (2015)
2015
Cited alongside, same era.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: NIPS. pp. 91–99 (2015)
2015
Cited alongside, same era.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: CVPR. pp. 1–9 (2015)

Then

He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: ICCV. pp. 2980–2988 (2017)
2017
Later among the works it cites.
Jiang, L., Zhou, Z., Leung, T., Li, L.J., Fei-Fei, L.: Mentornet: Regularizing very deep neural networks on corrupted labels. CoRR abs/1712.05055
Original
2017
Later among the works it cites.
Lee, K.H., He, X., Zhang, L., Yang, L.: Cleannet: Transfer learning for scalable image classifier training with label noise. CoRR abs/1711.07131
Original
2017
Later among the works it cites.
Li, W., Wang, L., Li, W., Agustsson, E., Berent, J., Gupta, A., Sukthankar, R., Van Gool, L.: Webvision challenge: Visual learning and understanding with web data. CoRR abs/1705.05640
Original
2017
Later among the works it cites.
Li, W., Wang, L., Li, W., Agustsson, E., Van Gool, L.: Webvision database: Visual learning and understanding from web data. CoRR abs/1708.02862

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2015

Cited alongside, same era.

Xiao, T., Xia, T., Yang, Y., Huang, C., Wang, X.: Learning from massive noisy labeled data for image classification. In: CVPR. pp. 2691–2699 (2015)

2015

Cited alongside, same era.

Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. CoRR abs/1606.00915

2016

Cited alongside, same era.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2016), cVPR

2016

Cited alongside, same era.

I. Misra, C. L. Zitnick, M.M., Girshick, R.: Seeing through the human reporting bias: Visual classifiers from noisy human-centric labels. In: CVPR (2016)

2016

Cited alongside, same era.

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: Ssd: Single shot multibox detector. In: ECCV. pp. 21–37. Springer (2016)

2016

Cited alongside, same era.

Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR. pp. 779–788 (2016)

2016

Cited alongside, same era.

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR. pp. 2818–2826 (2016)

2016

Cited alongside, same era.

2017

Later among the works it cites.

Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection pp. 2980–2988 (2017)

2017

Later among the works it cites.

Pandey, P., Deepthi, A., Mandal, B., Puhan, N.: Foodnet: Recognizing foods using ensemble of deep networks. IEEE Signal Processing Letters 24

2017

Later among the works it cites.

Patrini, G., Rozza, A., Menon, A.K., Nock, R., Qu, L.: Making deep neural networks robust to label noise: a loss correction approach pp. 1944–1952 (2017)

2017

Later among the works it cites.

Rolnick, D., Veit, A., Belongie, S., Shavit, N.: Deep learning is robust to massive label noise. CoRR abs/1705.10694

2017

Later among the works it cites.

Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI. pp. 4278–4284 (2017)

2017

Later among the works it cites.

Veit, A., Alldrin, N., Chechik, G., Krasin, I., Gupta, A., Belongie, S.: Learning from noisy large-scale datasets with minimal supervision. In: CVPR (2017)

2017

Later among the works it cites.

Wang, L., Guo, S., Huang, W., Xiong, Y., Qiao, Y.: Knowledge guided disambiguation for large-scale scene classification with multi-resolution cnns. IEEE Trans. on Image Processing (TIP) 26

2017

Later among the works it cites.

Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. CVPR (2018)

2018

Closest in time.