Multi Label Loss Correction against Missing and Corrupted Labels

Missing and corrupted labels can significantly ruin the learning process and, consequently, the classifier performance. Multi-label learning where each instance is tagged with variable number of labels is particularly affected. Although missing labels (false-negatives) is a well-studied problem in multi-label learning, it is considerably more challenging to have both false-negatives (missing labels) and false-positives (corrupted labels) simultaneously in multi-label datasets. In this paper, we propose Multi-Label Loss with Self Correction (MLLSC) which is a loss robust against coincident missing and corrupted labels. MLLSC computes the loss based on the true-positive (true-negative) or false-positive (false-negative) labels and deep neural network expertise. To distinguish between false-positive (false-negative) and true-positive (true-negative) labels, we use the output probability of the deep neural network during the learning process. Our method As MLLSC can be combined with different types of multi-label loss functions, we also address the label imbalance problem of multi-label datasets. Empirical evaluation on real-world vision datasets, i.e., MS-COCO, and MIR-FLICKR, shows that our method under medium (0.3) and high (0.6) corrupted and missing label probabilities outperform the state-of-the-art methods by, on average 23.97% and 9.31% mean average precision (mAP) points, respectively.