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Artificial intelligence (AI) models for computer vision trained with supervised machine learning are assumed to solve classification tasks by imitating human behavior learned from training labels.
Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
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Multi-class active learning for image classification
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Exemplary natural images explain CNN activations better than state-of-the-art feature visualization
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Learning with instance-dependent label noise: A sample sieve approach
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An image is worth 16x16 words: Transformers for image recognition at scale
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Imagenet classification with deep convolutional neural networks
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Amazon mechanical turk
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Interactive object detection
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Human object-similarity judgments reflect and transcend the primate-IT object representation
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Learning with noisy labels
Natarajan, N.; Dhillon, I. S.; Ravikumar, P. K.; and Tewari, A. 2013 · 2013
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Neural mechanisms underlying visual object recognition
Afraz, A.; Yamins, D. L.; and DiCarlo, J. J. 2014 · 2014
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Pixels to voxels: modeling visual representation in the human brain
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Structured labeling for facilitating concept evolution in machine learning
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Very deep convolutional networks for large-scale image recognition
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
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Classification with noisy labels by importance reweighting
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Combining crowd and expert labels using decision theoretic active learning
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Going deeper with convolutions
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Learning from massive noisy labeled data for image classification
Xiao, T.; Xia, T.; Yang, Y.; Huang, C.; and Wang, X. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Crowdsourcing in computer vision
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Informed Truthfulness in Multi-Task Peer Prediction
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Revolt: Collaborative crowdsourcing for labeling machine learning datasets
Chang, J. C.; Amershi, S.; and Kamar, E. 2017 · 2017
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Real time image saliency for black box classifiers
Dabkowski, P.; and Gal, Y. 2017 · 2017
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Explaining nonlinear classification decisions with deep taylor decomposition
Montavon, G.; Lapuschkin, S.; Binder, A.; Samek, W.; and Müller, K.-R. 2017 · 2017
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On human intellect and machine failures: Troubleshooting integrative machine learning systems
Nushi, B.; Kamar, E.; Horvitz, E.; and Kossmann, D. 2017 · 2017
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Deep active learning for image classification
Ranganathan, H.; Venkateswara, H.; Chakraborty, S.; and Panchanathan, S. 2017 · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; and Batra, D. 2017 · 2017
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An empirical analysis of backward compatibility in machine learning systems
Srivastava, M.; Nushi, B.; Kamar, E.; Shah, S.; and Horvitz, E. 2020 · 2020
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Draw with me: Human-in-the-loop for image restoration
Weber, T.; Hußmann, H.; Han, Z.; Matthes, S.; and Liu, Y. 2020 · 2020
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Combating noisy labels by agreement: A joint training method with co-regularization
Wei, H.; Feng, L.; Chen, X.; and An, B. 2020 · 2020
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Does the whole exceed its parts? The effect of AI explanations on complementary team performance
Bansal, G.; Wu, T.; Zhou, J.; Fok, R.; Nushi, B.; Kamar, E.; Ribeiro, M. T.; and Weld, D. 2021 · 2021
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Transformer interpretability beyond attention visualization
Chefer, H.; Gur, S.; and Wolf, L. 2021 · 2021
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Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
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Smilkov, D.; Thorat, N.; Kim, B.; Viégas, F.; and Wattenberg, M. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Suggestive annotation: A deep active learning framework for biomedical image segmentation
Yang, L.; Zhang, Y.; Chen, J.; Zhang, S.; and Chen, D. Z. 2017 · 2017
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Co-teaching: Robust training of deep neural networks with extremely noisy labels
Han, B.; Yao, Q.; Yu, X.; Niu, G.; Xu, M.; Hu, W.; Tsang, I.; and Sugiyama, M. 2018 · 2018
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Activations of deep convolutional neural networks are aligned with gamma band activity of human visual cortex
Kuzovkin, I.; Vicente, R.; Petton, M.; Lachaux, J.-P.; Baciu, M.; Kahane, P.; Rheims, S.; Vidal, J. R.; and Aru, J. 2018 · 2018
Cited alongside, same era.
Towards accountable AI: Hybrid human-machine analyses for characterizing system failure
Nushi, B.; Kamar, E.; and Horvitz, E. 2018 · 2018
Cited alongside, same era.
Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks
Rajalingham, R.; Issa, E. B.; Bashivan, P.; Kar, K.; Schmidt, K.; and DiCarlo, J. J. 2018 · 2018
Cited alongside, same era.
Cheng, H.; Zhu, Z.; Li, X.; Gong, Y.; Sun, X.; and Liu, Y. 2021 · 2021
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Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning
Fornaciari, T.; Uma, A.; Paun, S.; Plank, B.; Hovy, D.; and Poesio, M. 2021 · 2021
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Beyond self-attention: External attention using two linear layers for visual tasks
Guo, M.-H.; Liu, Z.-N.; Mu, T.-J.; and Hu, S.-M. 2021 · 2021
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Iterative Human-in-the-Loop Discovery of Unknown Unknowns in Image Datasets
Han, L.; Dong, X.; and Demartini, G. 2021 · 2021
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The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
Nguyen, G.; Kim, D.; and Nguyen, A. 2021 · 2021
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Manipulating and measuring model interpretability
Poursabzi-Sangdeh, F.; Goldstein, D. G.; Hofman, J. M.; Wortman Vaughan, J. W.; and Wallach, H. 2021 · 2021
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A survey of deep active learning
Ren, P.; Xiao, Y.; Chang, X.; Huang, P.-Y.; Li, Z.; Gupta, B. B.; Chen, X.; and Wang, X. 2021 · 2021
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Multimodal self-supervised learning for medical image analysis
Taleb, A.; Lippert, C.; Klein, T.; and Nabi, M. 2021 · 2021
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MLP-mixer: An all-MLP architecture for vision
Tolstikhin, I. O.; Houlsby, N.; Kolesnikov, A.; Beyer, L.; Zhai, X.; Unterthiner, T.; Yung, J.; Steiner, A.; Keysers, D.; Uszkoreit, J.; et al. 2021 · 2021
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When Optimizing f-Divergence is Robust with Label Noise
Wei, J.; and Liu, Y. 2021 · 2021
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A Survey of Human-in-the-loop for Machine Learning
Wu, X.; Xiao, L.; Sun, Y.; Zhang, J.; Ma, T.; and He, L. 2021 · 2021
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A second-order approach to learning with instance-dependent label noise
Zhu, Z.; Liu, T.; and Liu, Y. 2021 · 2021
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How Well do Feature Visualizations Support Causal Understanding of CNN Activations?
Zimmermann, R. S.; Borowski, J.; Geirhos, R.; Bethge, M.; Wallis, T.; and Brendel, W. 2021 · 2021
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A survey of visual analytics for Explainable Artificial Intelligence methods
Alicioglu, G.; and Sun, B. 2022 · 2022
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Hive: evaluating the human interpretability of visual explanations
Kim, S. S.; Meister, N.; Ramaswamy, V. V.; Fong, R.; and Russakovsky, O. 2022 · 2022
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Robust training under label noise by over-parameterization
Liu, S.; Zhu, Z.; Qu, Q.; and You, C. 2022 · 2022
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Training language models to follow instructions with human feedback
Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; et al. 2022 · 2022
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Promix: Combating label noise via maximizing clean sample utility
Wang, H.; Xiao, R.; Dong, Y.; Feng, L.; and Zhao, J. 2022 · 2022
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Paddles: Phase-amplitude spectrum disentangled early stopping for learning with noisy labels
Huang, H.; Kang, H.; Liu, S.; Salvado, O.; Rakotoarivelo, T.; Wang, D.; and Liu, T. 2023 · 2023
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When does privileged information explain away label noise?
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To aggregate or not? learning with separate noisy labels
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Regularly truncated m-estimators for learning with noisy labels
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Robust data pruning under label noise via maximizing re-labeling accuracy
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Binary classification with confidence difference
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A bespoke question intent taxonomy for e-commerce
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Tackling vision language tasks through learning inner monologues
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