Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

Extreme multi-label text classification (XMTC) refers to the problem of assigning to each
document its most relevant subset of class labels from an extremely large label collection …

Target encoding plays a central role when learning Convolutional Neural Networks. In this
realm, one-hot encoding is the most prevalent strategy due to its simplicity. However, this so …

Attributes act as intermediate representations that enable parameter sharing between
classes, a must when training data is scarce. We propose to view attribute-based image …

Predicting all applicable labels for a given image is known as multi-label classification.
Compared to the standard multi-class case (where each image has only one label), it is …

We evaluate whether features extracted from the activation of a deep convolutional network
trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re …

Image classification has advanced significantly in recent years with the availability of large-
scale image sets. However, fine-grained classification remains a major challenge due to the …

Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

The objective in extreme multi-label learning is to train a classifier that can automatically tag
a novel data point with the most relevant subset of labels from an extremely large label set …