Multi-instance learning (MIL) deals with tasks where data consist of set of bags and each bag is represented by a set of instances. Only the bag labels are observed but the label for each instance is not available. Previous MIL studies typically assume that the training and test samples follow the same distribution, which is often violated in real-world applications. Existing methods address distribution changes by re-weighting the training data with the density ratio between the training and test samples. However, models are often trained without prior knowledge of the test distribution which renders existing methods inapplicable. Inspired by a connection between MIL and causal inference, we propose a novel framework for addressing distribution change in MIL without relying on the test distribution. Experimental results validate the effectiveness of our approach.
Several MIL algorithms have been proposed to address distribution change by utilizing the test distribution to calculate importance weight. MICS @cite_11 solves the problem by modeling the bag-level and instance-level distribution changes together. MIKI @cite_2 proposes that the distributions of positive instances should be modeled explicitly to address shifts of the positive concepts.
@cite_2 @cite_11
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We present a Multiple-Instance Learning (MIL) algorithm for determining the salience of each item in each bag with respect to the bag’s real-valued label. We use an alternating-projections constrained optimization approach to simultaneously learn a regression model and estimate all salience values. We evaluate this algorithm on a significant real-world problem, crop yield modeling, and demonstrate that it provides more extensive, intuitive, and stable salience models than Primary-Instance Regression, which selects a single relevant item from each bag. Relevant and irrelevant images collected from the Web (e.g., Flickr.com) have been employed as loosely labeled training data for image categorization and retrieval. In this work, we propose a new approach to learn a robust classifier for text-based image retrieval (TBIR) using relevant and irrelevant training web images, in which we explicitly handle noise in the loose labels of training images. Specifically, we first partition the relevant and irrelevant training web images into clusters. By treating each cluster as a “bag” and the images in each bag as “instances”, we formulate this task as a multi-instance learning problem with constrained positive bags, in which each positive bag contains at least a portion of positive instances. We present a new algorithm called MIL-CPB to effectively exploit such constraints on positive bags and predict the labels of test instances (images). Observing that the constraints on positive bags may not always be satisfied in our application, we additionally propose a progressive scheme (referred to as Progressive MIL-CPB, or PMIL-CPB) to further improve the retrieval performance, in which we iteratively partition the top-ranked training web images from the current MIL-CPB classifier to construct more confident positive “bags” and then add these new “bags” as training data to learn the subsequent MIL-CPB classifiers. Comprehensive experiments on two challenging real-world web image data sets demonstrate the effectiveness of our approach. We introduce a boosting framework for multiple instance learning (MIL) with varied aggregation of instances. In this framework, a diverse set of aggregation functions can be used to refine the notion of a positive bag for multiple instance learning. We investigate the effect of a wide range of orness in aggregation, using ordered weighted averaging. Thus, we obtain a new notion of a positive bag, which can represent different levels of ambiguity. We evaluate the performance of the proposed algorithm on popular MIL datasets. The experimental results show that this algorithm outperforms the standard MILBoost algorithm. We present MI-CRF, a conditional random field (CRF) model for multiple instance learning (MIL). MI-CRF models bags as nodes in a CRF with instances as their states. It combines discriminative unary instance classifiers and pairwise dissimilarity measures. We show that both forces improve the classification performance. Unlike other approaches, MI-CRF considers all bags jointly during training as well as during testing. This makes it possible to classify test bags in an imputation setup. The parameters of MI-CRF are learned using constraint generation. Furthermore, we show that MI-CRF can incorporate previous MIL algorithms to improve on their results. MI-CRF obtains competitive results on five standard MIL datasets.