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Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization, IEEE (November 2011) 2144–2151

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Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks, IEEE (June 2014) 1717–1724

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Return of the Devil in the Details: Delving Deep into Convolutional Nets, British Machine Vision Association (2014) 6.1–6.12

Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: · 2014

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Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., Sun, J.: · 2015

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A Deeper Look at Dataset Bias

Tommasi, T., Patricia, N., Caputo, B., Tuytelaars, T.: · 2015

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Parkhi, O., Vedaldi, A., Zisserman, A.: · 2015

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Ms-celeb-1m: Challenge of recognizing one million celebrities in the real world

Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: · 2016

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Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.W.: · 2017

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Adversarial Discriminative Domain Adaptation

Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: · 2017

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Domain-Adversarial Training of Neural Networks

Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: · 2017

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Gender shades: Intersectional accuracy disparities in commercial gender classification

Buolamwini, J., Gebru, T.: · 2018

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Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges