Next, we aim to build distributional implicit matchings between the domain-level preferences of two domains.
To improve the MEC performance, it is required to design an optimal offloading strategy that includes offloading decision (i. e., whether offloading or not) and computational resource allocation of MEC.
In this paper, we explicitly address the route planning for a group of agents, including UAVs, workers, and cars, with the goal of maximizing the task completion rate.
To address this limitation, we propose a Text-guided Unsupervised StyleGAN Latent Transformation (TUSLT) model, which adaptively infers a single transformation step in the latent space of StyleGAN to simultaneously manipulate multiple attributes on a given input image.
It is worth noting that we reduce the dependence of BPFRe on paired training samples by imposing effective regularization on unpaired ones.
We report extensive experiments on diverse datasets, scenarios, and platforms and demonstrate the superiority of AdaEnlight compared with state-of-the-art low-light image and video enhancement solutions.
In this study, we address these concerns by learning (1) multi-scale representations of short-term interests; and (2) dynamics-aware representations of long-term interests.
The intelligent dialogue system, aiming at communicating with humans harmoniously with natural language, is brilliant for promoting the advancement of human-machine interaction in the era of artificial intelligence.
On the one hand, we model the rich correlations between the users' diverse behaviors (e. g., answer, follow, vote) to obtain the individual-level behavior interaction.
To address these issues, we propose DeepExpress - a deep-learning based express delivery sequence prediction model, which extends the classic seq2seq framework to learning complex coupling between sequence and features.
To solve the imbalanced distribution problem, in this paper we propose TL-SDD: a novel Transfer Learning-based method for Surface Defect Detection.
Semi-supervised generative learning (SSGL) makes use of unlabeled data to achieve a trade-off between the data collection/annotation effort and generation performance, when adequate labeled data are not available.
There are many deep learning (e. g., DNN) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of human lives.
This paper proposes a strategy named IHMs (Intermediate Searching at Heuristic Moments): each step we figure out which moment is the best to search according to a heuristic indicator, then at that moment search locations one by one in descending order of predicted appearing probabilities, until a search hits; iterate this step until we get the object's current location.
Text generation system has made massive promising progress contributed by deep learning techniques and has been widely applied in our life.
In this study, we present a survey of the state-of-the-art deep learning methods for sensor-based human activity recognition.
We first give a brief review of the literature history of MID, based on which we present several new research challenges and techniques of it, including early detection, detection by multimodal data fusion, and explanatory detection.
In recent years, with the development of deep learning, text generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication.
A generic model for CrowdMining is further proposed based on a set of existing studies.
In fact, user's behaviors from different domains regarding the same items are usually relevant.
And the multiple agents in the proposed model represent activities with collective motions across body parts by independently selecting modalities associated with single motions.
In the last two decades, the landscape of text generation has undergone tremendous changes and is being reshaped by the success of deep learning.
To deal with these problems, a novel Inner-Imaging architecture is proposed in this paper, which allows relationships between channels to meet the above requirement.
Multimodal features play a key role in wearable sensor-based human activity recognition (HAR).
In order to adapt to the tongue image in a variety of photographic environments and construct herbal prescriptions, a neural network framework for prescription construction is designed.
To solve the cold-start problem, we propose CityTransfer, which transfers chain store knowledge from semantically-relevant domains (e. g., other cities with rich knowledge, similar chain enterprises in the target city) for chain store placement recommendation in a new city.
Multimodal features play a key role in wearable sensor based Human Activity Recognition (HAR).