ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases

30 November 2017

Papers citing "ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases"

15 / 15 papers shown

Title
When does dough become a bagel? Analyzing the remaining mistakes on ImageNet Vijay Vasudevan Benjamin Caine Raphael Gontijo-Lopes Sara Fridovich-Keil Rebecca Roelofs VLM UQCV 35 57 0 09 May 2022
A Systematic Study of Bias Amplification Melissa Hall L. V. D. van der Maaten Laura Gustafson Maxwell Jones Aaron B. Adcock 102 70 0 27 Jan 2022
A generalizable saliency map-based interpretation of model outcome Shailja Thakur S. Fischmeister AAML FAtt MILM 22 2 0 16 Jun 2020
Algorithmic Fairness Dana Pessach E. Shmueli FaML 30 387 0 21 Jan 2020
Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation Emily Dinan Angela Fan Adina Williams Jack Urbanek Douwe Kiela Jason Weston 22 205 0 10 Nov 2019
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications Nicholas Carlini Ulfar Erlingsson Nicolas Papernot OOD OODD 19 62 0 29 Oct 2019
Does Object Recognition Work for Everyone? Terrance Devries Ishan Misra Changhan Wang L. V. D. van der Maaten 33 261 0 06 Jun 2019
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models D. Su Huan Zhang Hongge Chen Jinfeng Yi Pin-Yu Chen Yupeng Gao VLM 15 387 0 05 Aug 2018
Troubling Trends in Machine Learning Scholarship Zachary Chase Lipton Jacob Steinhardt 21 288 0 09 Jul 2018
xGEMs: Generating Examplars to Explain Black-Box Models Shalmali Joshi Oluwasanmi Koyejo Been Kim Joydeep Ghosh MLAU 25 40 0 22 Jun 2018
Exploring the Limits of Weakly Supervised Pretraining D. Mahajan Ross B. Girshick Vignesh Ramanathan Kaiming He Manohar Paluri Yixuan Li Ashwin R. Bharambe L. V. D. van der Maaten VLM 59 1,356 0 02 May 2018
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning Nicolas Papernot Patrick D. McDaniel OOD AAML 8 502 0 13 Mar 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 56 1,791 0 30 Nov 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 284 5,835 0 08 Jul 2016