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Multi-label classification (MLC) is a generalization of standard classification where multiple labels may be assigned to a given sample.
What are statistical decisions
McNicol, D · 1972
Earlier work this paper cites.
A neural network classifier based on dempster-shafer theory
Denoeux, T · 2000
Earlier work this paper cites.
Optimizing search engines using clickthrough data
Joachims, T · 2002
Earlier work this paper cites.
Learning multi-label scene classification
Boutell, M. R., Luo, J., Shen, X., and Brown, C. M · 2004
Earlier work this paper cites.
Rcv1: A new benchmark collection for text categorization research
Lewis, D. D., Yang, Y., Rose, T. G., and Li, F · 2004
Earlier work this paper cites.
ML-KNN: A lazy learning approach to multi-label learning
Zhang, M.-L. and Zhou, Z.-H · 2007
Earlier work this paper cites.
Multilabel text classification for automated tag suggestion
Katakis, I., Tsoumakas, G., and Vlahavas, I · 2008
Earlier work this paper cites.
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2008
Earlier work this paper cites.
Cheap and fast–but is it good? evaluating non-expert annotations for natural language tasks
Snow, R., O’connor, B., Jurafsky, D., and Ng, A. Y · 2008
Earlier work this paper cites.
From annotator agreement to noise models
Beigman Klebanov, B. and Beigman, E · 2009
Earlier work this paper cites.
Nus-wide: A real-world web image database from national university of singapore
Chua, T.-S., Tang, J., Hong, R., Li, H., Luo, Z., and Zheng, Y · 2009
Earlier work this paper cites.
Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation
Guillaumin, M., Mensink, T., Verbeek, J., and Schmid, C · 2009
Earlier work this paper cites.
ebird: A citizen-based bird observation network in the biological sciences
Sullivan, B. L., Wood, C. L., Iliff, M. J., Bonney, R. E., Fink, D., and Kelling, S · 2009
Earlier work this paper cites.
Bayes optimal multilabel classification via probabilistic classifier chains
Cheng, W., Hüllermeier, E., and Dembczynski, K. J · 2010
Earlier work this paper cites.
Quality management on amazon mechanical turk
Ipeirotis, P. G., Provost, F., and Wang, J · 2010
Earlier work this paper cites.
Learning from crowds
Raykar, V. C., Yu, S., Zhao, L. H., Valadez, G. H., Florin, C., Bogoni, L., and Moy, L · 2010
Earlier work this paper cites.
Classifier chains for multi-label classification
Read, J., Pfahringer, B., Holmes, G., and Frank, E · 2011
Earlier work this paper cites.
Wsabie: Scaling up to large vocabulary image annotation
Weston, J., Bengio, S., and Usunier, N · 2011
Earlier work this paper cites.
A survey of crowdsourcing systems
Yuen, M.-C., King, I., and Leung, K.-S · 2011
Earlier work this paper cites.
Efficient multi-label classification with many labels
Bi, W. and Kwok, J · 2013
Earlier work this paper cites.
Classification in the presence of label noise: a survey
Frénay, B. and Verleysen, M · 2013
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
Earlier work this paper cites.
Deep convolutional ranking for multilabel image annotation
Gong, Y., Jia, Y., Toshev, A., Leung, T., and Ioffe, S · 2014
Earlier work this paper cites.
Large-scale multi-label text classification—revisiting neural networks
Nam, J., Kim, J., Mencía, E. L., Gurevych, I., and Fürnkranz, J · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D · 2014
Cited alongside, same era.
Sparse local embeddings for extreme multi-label classification
Bhatia, K., Jain, H., Kar, P., Varma, M., and Jain, P · 2015
Cited alongside, same era.
The pascal visual object classes challenge: A retrospective
Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., and Zisserman, A · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
Joint embedding of words and labels for text classification
Wang, G., Li, C., Wang, W., Zhang, Y., Shen, D., Zhang, X., Henao, R., and Carin, L · 2018
Later among the works it cites.
Partial multi-label learning
Xie, M.-K. and Huang, S.-J · 2018
Later among the works it cites.
Deep learning from noisy image labels with quality embedding
Yao, J., Wang, J., Tsang, I. W., Zhang, Y., Sun, J., Zhang, C., and Zhang, R · 2018
Later among the works it cites.
Generalized cross entropy loss for training deep neural networks with noisy labels
Zhang, Z. and Sabuncu, M. R · 2018
Later among the works it cites.
Multi-label image recognition with graph convolutional networks
Chen, Z.-M., Wei, X.-S., Wang, P., and Guo, Y · 2019
Later among the works it cites.
Breaking the glass ceiling for embedding-based classifiers for large output spaces
Guo, C., Mousavi, A., Wu, X., Holtmann-Rice, D. N., Kale, S., Reddi, S., and Kumar, S · 2019
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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Noise detection in the meta-learning level
Garcia, L. P., de Carvalho, A. C., and Lorena, A. C · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Fasttext. zip: Compressing text classification models
Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., and Mikolov, T · 2016
Cited alongside, same era.
Cnn-rnn: A unified framework for multi-label image classification
Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., and Xu, W · 2016
Cited alongside, same era.
Deep multi-species embedding
Chen, D., Xue, Y., Fink, D., Chen, S., and Gomes, C. P · 2017
Cited alongside, same era.
Later among the works it cites.
An effective label noise model for dnn text classification
Jindal, I., Pressel, D., Lester, B., and Nokleby, M · 2019
Later among the works it cites.
Efficient training on very large corpora via gramian estimation
Krichene, W., Mayoraz, N., Rendle, S., Zhang, L., Yi, X., Hong, L., Chi, E., and Anderson, J · 2019
Later among the works it cites.
Neural message passing for multi-label classification
Lanchantin, J., Sekhon, A., and Qi, Y · 2019
Later among the works it cites.
Partial multi-label learning by low-rank and sparse decomposition
Sun, L., Feng, S., Wang, T., Lang, C., and Jin, Y · 2019
Later among the works it cites.
Disentangled variational autoencoder based multi-label classification with covariance-aware multivariate probit model
Bai, J., Kong, S., and Gomes, C · 2020
Later among the works it cites.
Asymmetric loss for multi-label classification
Ben-Baruch, E., Ridnik, T., Zamir, N., Noy, A., Friedman, I., Protter, M., and Zelnik-Manor, L · 2020
Later among the works it cites.
Taming Pretrained Transformers for Extreme Multi-Label Text Classification , pp. 3163–3171
Chang, W.-C., Yu, H.-F., Zhong, K., Yang, Y., and Dhillon, I. S · 2020
Later among the works it cites.
Curriculum loss: Robust learning and generalization against label corruption
Lyu, Y. and Tsang, I. W · 2020
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Learning from noisy labels with deep neural networks: A survey, 2020
Song, H., Kim, M., Park, D., and Lee, J.-G · 2020
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Multi-label classification with label graph superimposing
Wang, Y., He, D., Li, F., Long, X., Zhou, Z., Ma, J., and Wen, S · 2020
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Multi-label classification with label graph superimposing
Wang, Y., He, D., Li, F., Long, X., Zhou, Z., Ma, J., and Wen, S · 2020
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Partial multi-label learning with noisy label identification
Xie, M.-K. and Huang, S.-J · 2020
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Cross-modality attention with semantic graph embedding for multi-label classification
You, R., Guo, Z., Cui, L., Long, X., Bao, Y., and Wen, S · 2020
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Partial multi-label learning via credible label elicitation
Zhang, M.-L. and Fang, J.-P · 2020
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Distilling effective supervision from severe label noise
Zhang, Z., Zhang, H., Arik, S. O., Lee, H., and Pfister, T · 2020
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Towards noise-resistant object detection with noisy annotations, 2021
Li, J., Xiong, C., and Hoi, S · 2021
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