As a promising area in machine learning, multi-task learning (MTL) aims to improve the
performance of multiple related learning tasks by leveraging useful information among them …

A crucial module of the widely applied machine learning (ML) model is the model training
phase, which involves large-scale training data, often including sensitive private data. ML …

In this paper, a novel knowledge distillation (KD)-based pedestrian attribute recognition
(PAR) model is developed, where a multi-label mixed feature learning network (MMFL-Net) …

Water is a natural resource for agricultural irrigation. Recycling use of water is important in
terms of resource conservation and is good for sustainable development of the ecological …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

Person re-identification (re-ID) and attribute recognition share a common target at learning
pedestrian descriptions. Their difference consists in the granularity. Most existing re-ID …

Multi-task learning in Convolutional Networks has displayed remarkable success in the field
of recognition. This success can be largely attributed to learning shared representations …

We propose a combined method that is based on the fuzzy time series (FTS) and
convolutional neural networks (CNN) for short-term load forecasting (STLF). Accordingly, in …

In this paper, we consider the problem of simultaneously detecting objects and inferring their
visual attributes in an image, even for those with no manual annotations provided at the …

Semantic understanding and localization are fundamental enablers of robot autonomy that
have been tackled as disjoint problems for the most part. While deep learning has enabled …