Bag encoding strategies in multiple instance learning problems

Kucukasci, Emel Seyma; Baydoğan, Mustafa Gökçe

doi:10.1016/j.ins.2018.08.020

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Bag space paradigm [6]: the classifier works with the whole bag by means of similarity functions. Embedded space paradigm [25]: the classifier transform the original space to a new embedded space, where the bags are represented as single attribute vectors.…”

Section: Classification In Multiple Instance Learningmentioning

confidence: 99%

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

Luengo

Sánchez-Tarragó

Prati

et al. 2021

Information Sciences

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Section: Classification In Multiple Instance Learningmentioning

confidence: 99%

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

Luengo

Sánchez-Tarragó

Prati

et al. 2021

Information Sciences

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“…Multiple instance learning (MIL), i.e., learning from ambiguous data (the labels are related to bags, not instances within the bags, meaning that we only have partial or incomplete knowledge about training instances), has been widely studied and applied to many challenging tasks, such as text categorization [1], object tracking [2], person re-identification [3], computer-aided medical diagnosis [4], etc. Therefore, it has received considerable attention, and various algorithms, for example APR [5], DD [6], Citation-KNN [7], EM-DD [8], MI-Kernel [9], miSVM and MISVM [10], DD-SVM [11], MILES [12], MissSVM [13], MIGraph and miGraph [14], MILIS [15], MILDS [16], MILEAGE [17], mi-DS [18], CK_MIL [19], SMILE [20], MIKI [21], TreeMIL [22], MILDM [23], mi-Net and MI-Net [24], Attention and Gated-Attention MIL [25], etc., have been proposed to deal with the MIL problem. However, there are two issues that hinder its practical application.…”

Section: Introductionmentioning

confidence: 99%

MILDMS: Multiple Instance Learning via DD Constraint and Multiple Part Similarity

Wen

Zhan

et al. 2019

Symmetry

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As a subject area of symmetry, multiple instance learning (MIL) is a special form of a weakly supervised learning problem where the label is related to the bag, not the instances contained in it. The difficulty of MIL lies in the incomplete label information of instances. To resolve this problem, in this paper, we propose a novel diverse density (DD) and multiple part similarity combination method for multiple instance learning, named MILDMS. First, we model the target concepts optimization with a DD function constraint on positive and negative instance space, which can greatly improve the robustness to label noise problem. Next, we combine the positive and negative instances in the bag (generated by hand-crafted and convolutional neural network features) with multiple part similarities to construct an MIL kernel. We evaluate the proposed approach on the MUSK dataset, whose results MUSK1 (91.9%) and MUSK2 (92.2%) show our method is comparable to other MIL algorithms. To further demonstrate generality, we also present experimental results on the PASCAL VOC 2007 and 2012 (46.5% and 42.2%) and COREL (78.6%) that significantly outperforms the state-of-the-art algorithms including deep MIL and other non-deep MIL algorithms.

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Concentration or distraction? A synergetic-based attention weights optimization method

Wang

et al. 2023

Complex Intell. Syst.

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The attention mechanism empowers deep learning to a broader range of applications, but the contribution of the attention module is highly controversial. Research on modern Hopfield networks indicates that the attention mechanism can also be used in shallow networks. Its automatic sample filtering facilitates instance extraction in Multiple Instances Learning tasks. Since the attention mechanism has a clear contribution and intuitive performance in shallow networks, this paper further investigates its optimization method based on the recurrent neural network. Through comprehensive comparison, we find that the Synergetic Neural Network has the advantage of more accurate and controllable convergences and revertible converging steps. Therefore, we design the Syn layer based on the Synergetic Neural Network and propose the novel invertible activation function as the forward and backward update formula for attention weights concentration or distraction. Experimental results show that our method outperforms other methods in all Multiple Instances Learning benchmark datasets. Concentration improves the robustness of the results, while distraction expands the instance observing space and yields better results. Codes available at https://github.com/wzh134/Syn.

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Bag encoding strategies in multiple instance learning problems

Cited by 5 publications

References 23 publications

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

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

MILDMS: Multiple Instance Learning via DD Constraint and Multiple Part Similarity

Concentration or distraction? A synergetic-based attention weights optimization method

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