Multi-Label Classification is the supervised learning problem where an instance may be associated with multiple labels. This is opposed to the traditional task of single-label classification (i.e., multi-class, or binary) where each instance is only associated with a single class label.
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Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency.
Ranked #1 on Multi-Label Classification on MS-COCO
Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.
Ranked #3 on Malware Detection on Android Malware Dataset
In particular, the prediction of aspect-sentiment pairs is converted into multi-label classification, aiming to capture the dependency between words in a pair.
Many Network Representation Learning (NRL) methods have been proposed to learn vector representations for vertices in a network recently.
Such applications demand prediction models with small storage and computational complexity that do not compromise significantly on accuracy.
The task of multi-label image recognition is to predict a set of object labels that present in an image.
It provides native Python implementations of popular multi-label classification methods alongside a novel framework for label space partitioning and division.
The field of medical diagnostics contains a wealth of challenges which closely resemble classical machine learning problems; practical constraints, however, complicate the translation of these endpoints naively into classical architectures.