For several benchmark datasets mostly from the multi-label image classification, we demonstrate that our generative model with proposed estimators can often yield superior prediction performance to existing met
As this post is geared more towards the theory + code behind multi-label image classification problems, we will use the subset dataset in theZero to GANs – Human Protein Classification. The dataset was created from the training set of the Human protein atlas dataset, and the number of classe...
Due to its superior label ambiguity analysis in datasets, multi-label learning has generated a sizeable amount of attention from various practical applications, such as text categorization [2,3], image annotation [4,5], and gene function classification [6]. In practical applications, apart from ...
Y. Zhao, and S. Yan, “HCP: a flexible CNN framework for multi-label image classification,”...
Multi-label image classificationCapsule networksDuring the past few years, single-label classification has been extensively studied. However, in public datasets, the number of multiple-labeled images is much larger than the number of...doi:10.1007/978-3-030-26763-6_14Diqi Pan...
In experimental evaluations, we used multi-labeled document and image datasets to evaluate classifiers, and then measured micro-averaged F-scores for eight classifiers. Even though we incrementally removed correct labels from the two datasets, the proposed algorithm tended to maintain the F-scores, ...
As our survey of related work shows above, recent approaches make few efforts to exploit the high-order class dependency, which constrains the performance in multi-label classification. Besides, direct utilization of CNNs pre-trained on natural image datasets (Zeggada et al., 2017, Koda et al...
31 papers with code • 0 benchmarks • 2 datasets Extreme Multi-Label Classification is a supervised learning problem where an instance may be associated with multiple labels. The two main problems are the unbalanced labels in the dataset and the amount of different labels....
While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects, scenes, actions and attributes in an image. Traditional ...
In multi-label datasets, information content is represented using the collaboration between the existing classes (or labels). Discriminative content representation is achieved by maximizing the inter-class margins. Using public-domain multi-label datasets, the proposed classification solution outperforms its...