Adaptive Active Learning for Image Classification

By Xin Li, Yuhong Guo. 2013

316 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors (2018, background): “Approaches include maximum entropy (Joshi et al., 2009), distance from the decision boundary (Tong and Koller, 2001), pseudo labelling high conﬁdence examples (Wang et al., 2017), and mixtures of information density and uncertainty measures (Li and Guo, 2013).”
Deep Bayesian Active Learning with Image Data (2017, methodology): “However Li & Guo (2013) fed low dimensional features (such as SIFT features) to their RBF kernel.”
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning (2020, methodology): “…identiﬁed the challenges of robust application and pitfalls faced through the use of heuristics (Roy and McCallum, 2001; Settles and Craven, 2008; Li and Guo, 2013), the latter are nonetheless once again dominant in the era of deep learning (Beluch et al., 2018; Geifman and El-Yaniv, 2019; Gal…”
The Power of Ensembles for Active Learning in Image Classification (2018, methodology): “Active learning concepts relevant for this paper include uncertainty sampling ([42], [32], [35]), query-by-committee ([30], [5]) and density-based approaches ([52], [61], [46]).”
Crowdsourcing in Computer Vision (2016, unknown): “Li and Guo [2013] propose to request labels on images that are both uncertain and lie in a dense”
Uncertainty in Deep Learning (2016, methodology): “image data, a task which has been extremely challenging so far with very sparse existing literature [Holub et al., 2008; Joshi et al., 2009; Li and Guo, 2013; Zhu et al., 2003].”
Variational Adversarial Active Learning (2019, methodology): “A hybrid framework combining uncertainty using conditional entropy and representation learning using information density was proposed in [31] for classiﬁcation tasks.”
Active Learning for Convolutional Neural Networks: A Core-Set Approach (2017, methodology): “It covers acquisition functions such as information theoretical methods (MacKay, 1992), ensemble approaches (McCallumzy & Nigamy, 1998; Freund et al., 1997) and uncertainty based methods (Tong & Koller, 2001; Joshi et al., 2009; Li & Guo, 2013).”
Active Learning for Efficient Audio Annotation and Classification with a Large Amount of Unlabeled Data (2019, background): “The effectiveness of AL has been shown in research areas such as natural language processing [9, 10], computer vision [11, 12], and speech recognition [13, 14].”
Learning by Asking Questions (2017, background): “Active learning (AL) involves a collection of unlabeled examples and a learner that selects which samples will be labeled by an oracle [26, 33, 48, 53].”
Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images (2018, methodology): “11,12,13 In the field of biomedical image classification, it was utilized for either generating better feature representation for images 14,15 or creating classification models 16,17,18,19 iteratively.”