1,127 research outputs found
Lost in Translation: Cross-Country Differences in Hotel Guest Satisfaction
With the global expansion of the hotel industry and greater mobility of international travelers, awareness of international differences in guests’ attitudes about their travel experiences is important. As a consequence, most multinational hotel chains currently invest significant resources in implementing large-scale measurement programs to track, compare, and benchmark guest satisfaction across their various international markets. They do so for two related reasons. First, most hoteliers understand that highly satisfied guests are much more likely to return to that property and spend more during future stays than guests who are indifferent or displeased.1 More important, successful hoteliers understand that simply tracking performance is not enough. What is required is using the results of tracking programs to guide day-to-day management decisions and, ultimately, long-term operational strategies
Beyond triplet loss: a deep quadruplet network for person re-identification
Person re-identification (ReID) is an important task in wide area video
surveillance which focuses on identifying people across different cameras.
Recently, deep learning networks with a triplet loss become a common framework
for person ReID. However, the triplet loss pays main attentions on obtaining
correct orders on the training set. It still suffers from a weaker
generalization capability from the training set to the testing set, thus
resulting in inferior performance. In this paper, we design a quadruplet loss,
which can lead to the model output with a larger inter-class variation and a
smaller intra-class variation compared to the triplet loss. As a result, our
model has a better generalization ability and can achieve a higher performance
on the testing set. In particular, a quadruplet deep network using a
margin-based online hard negative mining is proposed based on the quadruplet
loss for the person ReID. In extensive experiments, the proposed network
outperforms most of the state-of-the-art algorithms on representative datasets
which clearly demonstrates the effectiveness of our proposed method.Comment: accepted to CVPR201
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation
Pre-training and fine-tuning have achieved great success in the natural
language process field. The standard paradigm of exploiting them includes two
steps: first, pre-training a model, e.g. BERT, with a large scale unlabeled
monolingual data. Then, fine-tuning the pre-trained model with labeled data
from downstream tasks. However, in neural machine translation (NMT), we address
the problem that the training objective of the bilingual task is far different
from the monolingual pre-trained model. This gap leads that only using
fine-tuning in NMT can not fully utilize prior language knowledge. In this
paper, we propose an APT framework for acquiring knowledge from the pre-trained
model to NMT. The proposed approach includes two modules: 1). a dynamic fusion
mechanism to fuse task-specific features adapted from general knowledge into
NMT network, 2). a knowledge distillation paradigm to learn language knowledge
continuously during the NMT training process. The proposed approach could
integrate suitable knowledge from pre-trained models to improve the NMT.
Experimental results on WMT English to German, German to English and Chinese to
English machine translation tasks show that our model outperforms strong
baselines and the fine-tuning counterparts
Deep Reinforcement Learning-based Multi-objective Path Planning on the Off-road Terrain Environment for Ground Vehicles
Due to the energy-consumption efficiency between up-slope and down-slope is
hugely different, a path with the shortest length on a complex off-road terrain
environment (2.5D map) is not always the path with the least energy
consumption. For any energy-sensitive vehicles, realizing a good trade-off
between distance and energy consumption on 2.5D path planning is significantly
meaningful. In this paper, a deep reinforcement learning-based 2.5D
multi-objective path planning method (DMOP) is proposed. The DMOP can
efficiently find the desired path with three steps: (1) Transform the
high-resolution 2.5D map into a small-size map. (2) Use a trained deep Q
network (DQN) to find the desired path on the small-size map. (3) Build the
planned path to the original high-resolution map using a path enhanced method.
In addition, the imitation learning method and reward shaping theory are
applied to train the DQN. The reward function is constructed with the
information of terrain, distance, border. Simulation shows that the proposed
method can finish the multi-objective 2.5D path planning task. Also, simulation
proves that the method has powerful reasoning capability that enables it to
perform arbitrary untrained planning tasks on the same map
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