Yannan Liu

Yu Li, Muxi Chen, Yannan Liu et al.

Deep Active Learning (DAL) has been advocated as a promising method to reduce labeling costs in supervised learning. However, existing evaluations of DAL methods are based on different settings, and their results are controversial. To tackle this issue, this paper comprehensively evaluates 19 existing DAL methods in a uniform setting, including traditional fully-\underline{s}upervised \underline{a}ctive \underline{l}earning (SAL) strategies and emerging \underline{s}emi-\underline{s}upervised \underline{a}ctive \underline{l}earning (SSAL) techniques. We have several non-trivial findings. First, most SAL methods cannot achieve higher accuracy than random selection. Second, semi-supervised training brings significant performance improvement compared to pure SAL methods. Third, performing data selection in the SSAL setting can achieve a significant and consistent performance improvement, especially with abundant unlabeled data. Our findings produce the following guidance for practitioners: one should (i) apply SSAL early and (ii) collect more unlabeled data whenever possible, for better model performance.

Yu Li, Min Li, Qiuxia Lai et al.

Deep learning (DL) has achieved unprecedented success in a variety of tasks. However, DL systems are notoriously difficult to test and debug due to the lack of explainability of DL models and the huge test input space to cover. Generally speaking, it is relatively easy to collect a massive amount of test data, but the labeling cost can be quite high. Consequently, it is essential to conduct test selection and label only those selected "high quality" bug-revealing test inputs for test cost reduction. In this paper, we propose a novel test prioritization technique that brings order into the unlabeled test instances according to their bug-revealing capabilities, namely TestRank. Different from existing solutions, TestRank leverages both intrinsic attributes and contextual attributes of test instances when prioritizing them. To be specific, we first build a similarity graph on test instances and training samples, and we conduct graph-based semi-supervised learning to extract contextual features. Then, for a particular test instance, the contextual features extracted from the graph neural network (GNN) and the intrinsic features obtained with the DL model itself are combined to predict its bug-revealing probability. Finally, TestRank prioritizes unlabeled test instances in descending order of the above probability value. We evaluate the performance of TestRank on a variety of image classification datasets. Experimental results show that the debugging efficiency of our method significantly outperforms existing test prioritization techniques.

Bo Luo, Yannan Liu, Lingxiao Wei et al.

Machine learning systems based on deep neural networks, being able to produce state-of-the-art results on various perception tasks, have gained mainstream adoption in many applications. However, they are shown to be vulnerable to adversarial example attack, which generates malicious output by adding slight perturbations to the input. Previous adversarial example crafting methods, however, use simple metrics to evaluate the distances between the original examples and the adversarial ones, which could be easily detected by human eyes. In addition, these attacks are often not robust due to the inevitable noises and deviation in the physical world. In this work, we present a new adversarial example attack crafting method, which takes the human perceptual system into consideration and maximizes the noise tolerance of the crafted adversarial example. Experimental results demonstrate the efficacy of the proposed technique.