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Transfer learning is beneficial by allowing the expressive features of models pretrained on large-scale datasets to be finetuned for the target task of smaller, more domain-specific datasets.
DeCAF: A deep convolutional activation feature for generic visual recognition
Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, Ross Girshick Lubomir Bourdev, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, and Piotr Dollár · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nathan Srebro · 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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A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, and Simon Lacoste-Julien · 2017
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The Problem With Bias: Allocative Versus Representational Harms in Machine Learning
Solon Barocas, Kate Crawford, Aaron Shapiro, and Hanna Wallach · 2017
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
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No classification without representation: Assessing geodiversity issues in open data sets for the developing world
Shreya Shankar, Yoni Halpern, Eric Breck, James Atwood, Jimbo Wilson, and D. Sculley · 2017
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Men also like shopping: Reducing gender bias amplification using corpus-level constraints
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2017
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Gender recognition or gender reductionism? the social implications of embedded gender recognition systems
Foad Hamidi, Morgan Klaus Scheuerman, and Stacy M Branham · 2018
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Excavating ai:the politics of training sets for machine learning
Kate Crawford and Trevor Paglen · 2019
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Does object recognition work for everyone?
Terrance DeVries, Ishan Misra, Changhan Wang, and Laurens van der Maaten · 2019
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Rethinking imagenet pre-training
Kaiming He, Ross Girshick, and Piotr Dollár · 2019
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Using pre-training can improve model robustness and uncertainty
Dan Hendrycks, Kimin Lee, and Mantas Mazeika · 2019
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Inioluwa Deborah Raji Elena Spitzer, and Timnit Gebru · 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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Deep learning generalizes because the parameter-function map is biased towards simple functions
Guillermo Valle-Pérez, Chico Q. Camargo, and Ard A. Louis · 2019
Cited alongside, same era.
Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations
Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez · 2019
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
Lessons from archives: Strategies for collecting sociocultural data in machine learning
Eun Seo Jo and Timnit Gebru · 2020
Cited alongside, same era.
Do datasets have politics? disciplinary values in computer vision dataset development
Morgan Klaus Scheuerman, Alex Hanna, and Emily Denton · 2021
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Revisiting gendered web forms: An evaluation of gender inputs with (non-)binary people
Morgan Klaus Scheuerman, Aaron Jiang, Katta Spiel, and Jed R. Brubaker · 2021
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A step toward more inclusive people annotations for fairness
Candice Schumann, Susanna Ricco, Utsav Prabhu, Vittorio Ferrari, and Caroline Pantofaru · 2021
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Process for adapting language models to society (palms) with values-targeted datasets
Irene Solaiman and Christy Dennison · 2021
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Image representations learned with unsupervised pre-training contain human-like biases
Ryan Steed and Aylin Caliskan · 2021
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Directional bias amplification
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The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, Tom Duerig, and Vittorio Ferrari · 2020
Cited alongside, same era.
Rethinking the hyperparameters for fine-tuning
Hao Li, Pratik Chaudhari, Hao Yang, Michael Lam, Avinash Ravichandran, Rahul Bhotika, and Stefano Soatto · 2020
Cited alongside, same era.
Learning from failure: Training debiased classifier from biased classifier
Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, and Jinwoo Shin · 2020
Cited alongside, same era.
How useful is self-supervised pretraining for visual tasks?
Alejandro Newell and Jia Deng · 2020
Cited alongside, same era.
An investigation of why overparameterization exacerbates spurious correlations
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, and Percy Liang · 2020
Cited alongside, same era.
The pitfalls of simplicity bias in neural networks
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, and Praneeth Netrapalli · 2020
Cited alongside, same era.
Don’t judge an object by its context: Learning to overcome contextual bias
Krishna Kumar Singh, Dhruv Mahajan, Kristen Grauman, Yong Jae Lee, Matt Feiszli, and Deepti Ghadiyaram · 2020
Cited alongside, same era.
Angelina Wang and Olga Russakovsky · 2021
Later among the works it cites.
Understanding and evaluating racial biases in image captioning
Dora Zhao, Angelina Wang, and Olga Russakovsky · 2021
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Picking on the same person: Does algorithmic monoculture lead to outcome homogenization?
Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, and Percy Liang · 2022
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On the intrinsic and extrinsic fairness evaluation metrics for contextualized language representations
Yang Trista Cao, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta, Varun Kumar, Jwala Dhamala, and Aram Galstyan · 2022
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Vision models are more robust and fair when pretrained on uncurated images without supervision
Priya Goyal, Quentin Duval, Isaac Seessel, Mathilde Caron, Ishan Misra, Levent Sagun, Armand Joulin, and Piotr Bojanowski · 2022
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Fairness indicators for systematic assessments of visual feature extractors
Priya Goyal, Adriana Romero Soriano, Caner Hazirbas, Levent Sagun, and Nicolas Usunier · 2022
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The dollar street dataset: Images representing the geographic and socioeconomic diversity of the world
William A. Gaviria Rojas, Sudnya Diamos, Keertan Ranjan Kini, David Kanter, Vijay Janapa Reddi, and Cody Coleman · 2022
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When does bias transfer in transfer learning?
Hadi Salman, Saachi Jain, Andrew Ilyas, Logan Engstrom, Eric Wong, and Aleksander Madry · 2022
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A study on the distribution of social biases in self-supervised learning visual models
Kirill Sirotkin, Pablo Carballeira, and Marcos Escudero-Viñolo · 2022
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Worst of both worlds: Biases compound in pre-trained vision-and-language models
Tejas Srinivasan and Yonatan Bisk · 2022
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Upstream mitigation is not all you need: Testing the bias transfer hypothesis in pre-trained language models
Ryan Steed, Swetasudha Panda, and Michael Wick Ari Kobren · 2022
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Measuring representational harms in image captioning
Angelina Wang, Solon Barocas, Kristen Laird, and Hanna Wallach · 2022
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REVISE: A tool for measuring and mitigating bias in visual datasets
Angelina Wang, Alexander Liu, Ryan Zhang, Anat Kleiman, Leslie Kim, Dora Zhao, Iroha Shirai, Arvind Narayanan, and Olga Russakovsky · 2022
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Last layer re-training is sufficient for robustness to spurious correlations
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2023
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A whac-a-mole dilemma: Shortcuts come in multiples where mitigating one amplifies others
Zhiheng Li, Ivan Evtimov, Albert Gordo, Caner Hazirbas, Tal Hassner, Cristian Canton Ferrer, Chenliang Xu, and Mark Ibrahim · 2023
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Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset
Vikram V. Ramaswamy, Sing Yu Lin, Dora Zhao, Aaron B. Adcock, Laurens van der Maaten, Deepti Ghadiyaram, and Olga Russakovsky · 2023
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Men also do laundry: Multi-attribute bias amplification
Dora Zhao, Jerone T.A. Andrews, and Alice Xiang · 2023
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