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Visual search, recommendation, and contrastive similarity learning power technologies that impact billions of users worldwide.
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A simple framework for contrastive learning of visual representations
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Assessing bias in search engines
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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Learning a similarity metric discriminatively, with application to face verification
Sumit Chopra, Raia Hadsell, and Yann LeCun · 2005
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Search engine bias and the demise of search engine utopianism
Eric Goldman · 2005
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Through the google goggles: Sociopolitical bias in search engine design
Alejandro Diaz · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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The bellkor solution to the netflix grand prize
Yehuda Koren · 2009
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A survey of collaborative filtering techniques
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Slic superpixels
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The pascal visual object classes (voc) challenge
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Running experiments on amazon mechanical turk
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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Interpreting random forests, Oct 2014
Ando Saabas · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Explaining prediction models and individual predictions with feature contributions
Erik Štrumbelj and Igor Kononenko · 2014
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Values of non-atomic games
Robert J Aumann and Lloyd S Shapley · 2015
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
Counterfactual visual explanations
Yash Goyal, Ziyan Wu, Jan Ernst, Dhruv Batra, Devi Parikh, and Stefan Lee · 2019
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Cross attention network for few-shot classification
Ruibing Hou, Hong Chang, Bingpeng Ma, Shiguang Shan, and Xilin Chen · 2019
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Understanding searches better than ever before, Oct 2019
Pandu Nayak · 2019
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Interpretml: A unified framework for machine learning interpretability
Harsha Nori, Samuel Jenkins, Paul Koch, and Rich Caruana · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Anupam Datta, Shayak Sen, and Yair Zick · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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”why should I trust you?”: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Learning Deep Features for Discriminative Localization
B. Zhou, A. Khosla, Lapedriza. A., A. Oliva, and A. Torralba · 2016
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Beyond text queries: Searching with bing visual search, Jun 2017
Bing · 2017
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Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
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Exs: Explainable search using local model agnostic interpretability
Jaspreet Singh and Avishek Anand · 2019
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Visual explanation for deep metric learning
Sijie Zhu, Taojiannan Yang, and Chen Chen · 2019
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Axiom-based grad-cam: Towards accurate visualization and explanation of cnns
Ruigang Fu, Qingyong Hu, Xiaohu Dong, Yulan Guo, Yinghui Gao, and Biao Li · 2020
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Mark Hamilton, Stephanie Fu, William T Freeman, and Mindren Lu · 2020
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Explaining explanations: Axiomatic feature interactions for deep networks
Joseph D Janizek, Pascal Sturmfels, and Su-In Lee · 2020
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Interpretable machine learning
Christoph Molnar · 2020
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Revisiting training strategies and generalization performance in deep metric learning, 2020
Karsten Roth, Timo Milbich, Samarth Sinha, Prateek Gupta, Björn Ommer, and Joseph Paul Cohen · 2020
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Mining cross-image semantics for weakly supervised semantic segmentation
Guolei Sun, Wenguan Wang, Jifeng Dai, and Luc Van Gool · 2020
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The shapley taylor interaction index
Mukund Sundararajan, Kedar Dhamdhere, and Ashish Agarwal · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors · 2020
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Multi-modality cross attention network for image and sentence matching
Xi Wei, Tianzhu Zhang, Yan Li, Yongdong Zhang, and Feng Wu · 2020
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Towards visually explaining similarity models
Meng Zheng, Srikrishna Karanam, Terrence Chen, Richard J Radke, and Ziyan Wu · 2020
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Transformer interpretability beyond attention visualization
Hila Chefer, Shir Gur, and Lior Wolf · 2021
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Moco: Momentum contrast for unsupervised visual representation learning
Kaiming He and Yuxin Wu · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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