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Labeling data for classification requires significant human effort. To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label. Computer algorithms are then used to infer the label for each instance in a bag, a process referred to as instance annotation. This task is challenging due to the ambiguity regarding the instance labels. We propose...
Multi-instance multi-label learning (MIML) is a framework that addresses label ambiguity when data contains bags, each bag contains instances, and a bag label set is provided for each bag. Instance annotation in the MIML setting is the problem of finding an instance level classifier given training data consisting of labeled bags of instances. Current approaches for instance annotation mainly focus...
A common framework of identifying bird species from audio recordings involves detecting bird song segments, which will be subsequently input to a classifier. In-field recordings are contaminated with various environmental noise. For such recordings, supervised segmentation has been observed to outperform unsupervised energy-based approaches. Prior supervised segmentation work considers only pixel-level...
The cost associated with manually labeling every individual instance in large datasets is prohibitive. Significant labeling efforts can be saved by assigning a collective label to a group of instances (a bag). This setup prompts the need for algorithms that allow labeling individual instances (instance annotation) based on bag-level labels. Probabilistic models in which instance-level labels are latent...
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