Multiple instance classification: Bag noise filtering for negative instance noise cleaning

Luengo, Julián; Sánchez-Tarragó, Dánel; Prati, Ronaldo C.; Herrera, Francisco

doi:10.1016/j.ins.2021.07.076

Cited by 9 publications

(2 citation statements)

References 41 publications

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“…Multi-instance Noise. Obtaining multi-instance model that are robust to perturbation/noise has been an active topic of research (Chevaleyre and Zucker 2000;Luengo et al 2021).…”

Section: Related Studies and Preliminariesmentioning

confidence: 99%

“…Third, it should be able to handle the bag noise. It is inevitable that the provided multi-instance bags are incomplete and noisy in real-world scenarios (Chevaleyre and Zucker 2000; Luengo et al 2021). Hence, developing robust multi-instance learning models to resist unnoticeable perturbation (e.g., missing or error instances in bags) is of significant importance.…”

Section: Introductionmentioning

confidence: 99%

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Robust Self-Supervised Multi-Instance Learning with Structure Awareness

Wang

Zhao

Wang

et al. 2023

AAAI

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Multi-instance learning (MIL) is a supervised learning where each example is a labeled bag with many instances. The typical MIL strategies are to train an instance-level feature extractor followed by aggregating instances features as bag-level representation with labeled information. However, learning such a bag-level representation highly depends on a large number of labeled datasets, which are difficult to get in real-world scenarios. In this paper, we make the first attempt to propose a robust Self-supervised Multi-Instance LEarning architecture with Structure awareness (SMILEs) that learns unsupervised bag representation. Our proposed approach is: 1) permutation invariant to the order of instances in bag; 2) structure-aware to encode the topological structures among the instances; and 3) robust against instances noise or permutation. Specifically, to yield robust MIL model without label information, we augment the multi-instance bag and train the representation encoder to maximize the correspondence between the representations of the same bag in its different augmented forms. Moreover, to capture topological structures from nearby instances in bags, our framework learns optimal graph structures for the bags and these graphs are optimized together with message passing layers and the ordered weighted averaging operator towards contrastive loss. Our main theorem characterizes the permutation invariance of the bag representation. Compared with state-of-the-art supervised MIL baselines, SMILEs achieves average improvement of 4.9%, 4.4% in classification accuracy on 5 benchmark datasets and 20 newsgroups datasets, respectively. In addition, we show that the model is robust to the input corruption.

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“…Multi-instance Noise. Obtaining multi-instance model that are robust to perturbation/noise has been an active topic of research (Chevaleyre and Zucker 2000;Luengo et al 2021).…”

Section: Related Studies and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%