Below we describe a few other prevalent multi-label datasets and explain how the ML48S differs800 from them, hence they were excluded from comparison in this paper.801 PASCALVOC [11] was created for object detection and classification, covering 20 basic-level802 classes across 4,574 images, with most images containing a single prominent object. This dataset is803 much smaller than ML48S and also contains much fewer classes which are all coarse-grained.804 VG500 is a modification of the Visual Genome dataset [19], a dataset focused on dense annotations805 linking images to respective captions. This dataset is not intended to be bounded by categories806 but has open-vocabulary annotations.

L48 stands out among similar datasets as being at country-wide, year-round scale while still maintaining high-quality bounding box annotations (see Table 1a).

Single-positive multi-label learning (SPMLL) is a weakly supervised multi-label learning problem, where each training example is annotated with only one positive label. Existing SPMLL methods typically assign pseudo-labels to unannotated labels with the assumption that prior probabilities of all classes are identical. However, the class-prior of each category may differ significantly in real-world scenarios, which makes the predictive model not perform as well as expected due to the unrealistic assumption on real-world application. To alleviate this issue, a novel framework named CRISP, i.e., Class-pRiors Induced Single-Positive multi-label learning, is proposed. Specifically, a class-priors estimator is introduced, which can estimate the class-priors that are theoretically guaranteed to converge to the groundtruth class-priors. In addition, based on the estimated class-priors, an unbiased risk estimator for classification is derived, and the corresponding risk minimizer can be guaranteed to approximately converge to the optimal risk minimizer on fully supervised data. Experimental results on ten MLL benchmark datasets demonstrate the effectiveness and superiority of our method over existing SPMLL approaches.

Hyponatremia: Predict whether a hyponatremia lab comes back as normal (>=135 mmol/L), mild (>=130 and <135 mmol/L), moderate (>=125 and <130 mmol/L), or severe (<125 mmol/L). We consider all lab results coded as LOINC/LG11363-5, LOINC/2951-2, or LOINC/2947-0. Anemia: Predict whether an anemia lab comes back as normal (>=120 g/L), mild (>=110 and <120 g/L), moderate (>=70 and <110 g/L), or severe (<70 g/L). We consider all lab results coded as LOINC/LP392452-1. Please note that for the results of our baseline experiments in Section 5, we reframe these lab value tasks as binary classification tasks, where a label is "negative" if the result is normal and "positive" otherwise.

Neural Information Processing Systems http://nips.cc/

Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data.

Experiments on twelve corrupted MLL datasets show the effectiveness of SMILEoverseveral existing SPMLL approaches.