Published as a conference paper at ICLR 2018 LEARNING FROM NOISY SINGLY-LABELED DATA
Supervised learning depends on annotated examples, which are taken to be the ground truth. But these labels often come from noisy crowdsourcing platforms, like Amazon Mechanical Turk. Practitioners typically collect multiple labels per example and aggregate the results to mitigate noise (the classic crowdsourcing problem). Given a fixed annotation budget and unlimited unlabeled data, redundant annotation comes at the expense of fewer labeled examples. This raises two fundamental questions: (1) How can we best learn from noisy workers? (2) How should we allocate our labeling budget to maximize the performance of a classifier? We propose a new algorithm for jointly modeling labels and worker quality from noisy crowd-sourced data. The alternating minimization proceeds in rounds, estimating worker quality from disagreement with the current model and then updating the model by optimizing a loss function that accounts for the current estimate of worker quality. Unlike previous approaches, even with only one annotation per example, our algorithm can estimate worker quality. We establish a generalization error bound for models learned with our algorithm and establish theoretically that it's better to label many examples once (vs less multiply) when worker quality exceeds a threshold. Experiments conducted on both ImageNet (with simulated noisy workers) and MS-COCO (using the real crowdsourced labels) confirm our algorithm's benefits.
This paper focuses on the learning-from-crowds problem when there is only one (or very few) noisy label per item. The main framework is based on the Dawid-Skene model. By jointly update the classifier weights and the confusion matrices of workers, the predictions of the classifier can help on the estimation problem with rare crowdsourced labels. The paper discusses the influence of the label redundancy both theoretically and empirically. Results show that with a fixed budget, it’s better to label many examples once rather than fewer examples multiple times.
The model and algorithm in this paper are simple and straightforward. However, I like the motivation of this paper and the discussion about the relationship between training efficiency and label redundancy. The problem of label aggregation with low redundancy is common in practice but hardly be formally analyzed and discussed. The conclusion that labeling more examples once is better can inspire other researchers to find more efficient ways to improve crowdsourcing.
About the technique details, this paper is clearly written, but some experimental comparisons and claims are not very convincing. Here I list some of my questions:
+About the MBEM algorithm, it’s better to make clear the difference between MBEM and a standard EM. Will it always converge? What’s its objective?
+The setting of Theorem 4.1 seems too simple. Can the results be extended to more general settings, such as when workers are not identical?
+When n = O(m log m), the result that \epslon_1 is constant is counterintuitive, people usually think large redundancy r can bring benefits on estimation, can you explain more on this?
+During CIFAR-10 experiments when r=1, each example only have one label. For the baselines weighted-MV and weighted-EM, they can only be directly trained using the same noisy labels. So can you explain why their performance is slightly different in most settings? Is it due to the randomly chosen procedure of the noisy labels?
+For ImageNet and MS-COCO experiments with a fixed budget, you reduced the training set when increasing the redundancy, which is unfair. The reduction of performance could mainly cause by seeing fewer raw images, but not the labels. It’s better to train some semi-supervised model to make the settings more comparable.